Early and late neonatal mortality in term newborns: Survival differences according to public and private hospitals in Brazil

Early and late neonatal mortality in term newborns: Survival differences according to public and private hospitals in Brazil | 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 Early and late neonatal mortality in term newborns: Survival differences according to public and private hospitals in Brazil Alejandra Andrea Roman Lay, Maria Elizangela Ramos Junqueira, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4477653/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: A better understanding of neonatal mortality risk factors in Brazil would guide improvements in these indicators. Thus, this study seeks to identify risk factors associated with early and late neonatal mortality stratified by public and private hospitals. Methods: This is a cohort study of newborns between January 1, 2012 and December 31, 2017. Mortality data were obtained through linkage between two Brazilian national government databases from São Paulo city. Cox regression models were used to estimate the associations between maternal and newborn characteristics on ENM (0-6 days) and LNM (7-27 days). Results : In the public sector, mother's age (≥35), gestational age (<38 and ≥41 weeks), inadequate and intermediate Kotelchuk index, cesarean section and low birth weight (LBW) were risk factors for ENM. In the private sector, mother's skin color (black), inadequate Kotelchuk index, parity (2 or more) and LBW were risk factors for ENM, as for the mother's education (university), gestational age of 39 weeks and female sex of the newborn were protective factors for ENM. Furthermore, in the public sector, mother's age (≤19), gestational age (< 38 weeks), inadequate and intermediate Kotelchuk index, cesarean section and LBW were risk factors for LMN. While in the private sector gestational age of ≤ 37 weeks and LBW were risk factors for LNM, on the other hand, mother's education (university) and female sex remain protective factors for LNM. Conclusions: In Brazil, there are differences in mother's characteristics and newborn between women cared for in the public and private sectors that could influence neonatal mortality. Neonatal mortality caesarean section public hospitals Figures Figure 1 BACKGROUND Neonatal mortality (NM) can be subdivided in early neonatal mortality (ENM) and late neonatal mortality (LNM). ENM refers to the number of deaths occurring during the first 6 days of life, while LNM are those that occur from day 7 to 27 of birth [ 1 ]. In Brazil, NM has been decreasing from 2000 to 2020, from 16.7 deaths/1000 live births (lb) to 8.8 deaths/1000 lb respectively [ 2 ]. However, it continues to be a public health challenge. NM is influenced by maternal socioeconomic factors [ 3 ], prenatal care [ 4 ], type of delivery (vaginal or caesarean) and characteristics of the place of birth; e.g. some studies suggest that the risk of NM is higher in public hospitals than in private ones [ 5 – 6 ]. Public hospitals in Brazil belong to the unified health system (Sistema Único de Saúde, SUS) that promotes universal health coverage. Health inequalities increased with decreased public funding and with the unmet health needs due to the political and economic crises suffered since 2016 [ 7 ]. These reflect the situation prior to the COVID-19 pandemic, which collapsed even more the Brazilian health system [ 8 ]. A better understanding of NM risk factors that are sensitive to socioeconomic changes and health inequalities in Brazil, would allow improvements to public prevention of NM and a reduction of regional inequalities [ 9 – 10 ]. This study aimed to identify the risk factors for early and late neonatal mortality with a focus on determinants related to childbirth in public and private hospitals, in newborns at term in the city of São Paulo, Brazil, including socio-demographic, obstetric and health care characteristics. METHODS Study population and data collection This is a cohort study of newborn in the city of São Paulo with follow-up from January 1, 2012 to December 31, 2017 (Fig. 1 ). We use prenatal and socioeconomic data of live births certificates (DN) from the Brazilian live births information system (SINASC) managed by the Epidemiology and Information Coordination (CEInfo) of the São Paulo Municipal Health Department, linked to death records contained in the Death Certificate (DO) of the Brazilian Mortality System (SIM). We obtain data from babies born between January 1, 2012, and December 31, 2017, followed until December 31, 2018. The individual records of the two sets of data were deterministically and probabilistically paired and linked through the variables DN number, child's sex and date of birth. In total, 13,347 pairs were found. More details about the linkage process have been previously described by Queiroz et al [ 11 ]. Between 2012 and 2017, 1,202,843 births were registered in the city of São Paulo. Of which we excluded 4,446 women without information on gestational age in weeks, totaling 1,198,397 women. Since our focus were the term babies, we excluded preterm pregnancies (gestational age of less than 37 weeks, n = 133,478) and post term (over 42 weeks, n = 15,757), 7 newborns without information on the day of birth and 6,383 missing data on type of hospital. Thus, the total sample for analysis was 1,042,772 women (Fig. 1 ). Outcome The outcome was NM, considered as all deaths from birth to 27 complete days of birth. This was divided into ENM (0–6 days) and LNM (7–27). All-cause mortality was obtained through linkage between SINASC and SIM. The classification of deaths was obtained according to the underlying cause of death, categorized by the International Classification of Diseases and Related Health Problems (ICD-10). Predictors The characteristics of the mothers included were maternal age in years (≤ 19, 20–34, ≥ 35), schooling (elementary, high, university), lives with a partner (yes, no) and skin color (black, non-black). In Brazil, the racial classification system follows the standard proposed by IBGE (Brazilian Institute of Geography and Statistics), classifying people as white, brown, black, oriental and indigenous people. In this study, we categorized race as black and non-black since studies indicate that in Brazil early neonatal mortality is highest for babies born to black women [ 12 ]. Non-black included white, oriental, indigenous and brown people. Other variables related to pregnancy and delivery were gestational age (37,38,39,40,41 completed weeks), parity (0, 1, 2 or more) and type of delivery (vaginal or caesarean). We also included the evaluation of adequate prenatal control through the modified kotelchuck index (MKI). This index is based on information about the month in which prenatal care (PNC) begins and the number of prenatal visits during the pregnancy. MKI classified women into: without prenatal visits, inadequate (onset of PNC > 3 months or onset of PNC < 4 months but < 3 prenatal visits), intermediate (onset of PNC < 4 months and number of prenatal visits between 3 to 5), adequate (onset of PNC < 4 months and number of prenatal visits equal to 6) and overadequate (onset of PNC < 4 months and number of prenatal visits greater than 6). Only 0.7% of the women did not have prenatal visits (n = 6,768). Thus, to ensure proper statistical analysis, these women were categorized as inadequate MKI. The newborn characteristics were low birth weight (LBW) (< 2500g versus ≥ 2500g) and sex assigned at birth (male or female). Data analysis The differences between the characteristics of the newborns by type of hospital and early, late and total neonatal mortality rates were calculated using the chi-square test. Cox regression models were performed to estimate the association between the independent variables and early and late neonatal mortality, stratified by public and private care. The results were expressed in Hazard ratios (HRs) with a confidence interval of 95% (95% CI). All analyses were performed using STATA 15 (Stata Corp., College Station, Texas, USA). RESULTS Out of the total term births, 72% reached up to 39 weeks of gestation. The majority were mothers between 20 and 34 years old (70%), non-black (93%) and primiparous (49%), and 74% of them had Kotelchuck index adequately. In relation to delivery, 57% were caesarean and 43% vaginal, while 54% were attended in the public hospital and 46% in the private hospital. There were significant differences between all baseline characteristics by type of hospital, except for sex of the baby (Table 1). Compared with private sector, all markers associated with social vulnerability were present in public sector: more adolescents, more black women, mother's lower education, higher number of mothers without partner and inadequate antenatal care. Additionally, there was more multiparity and low birthweight, and less cesarean sections. Between the years 2012 to 2017 there were a total of 2,023 neonatal deaths. Of which 1,213 corresponded to early neonatal deaths and 810 late neonatal deaths. There were significant differences in neonatal mortality according to sector. Mortality rates for both ENM and LNM in the public sector (2.5 *1000 births) were double those in the private sector (1.2*1000 births) (Table 2). Early neonatal mortality Public hospitals Neonates whose mothers were ≥35 years had increased risk of ENM compared with mothers aged 20 to 34 (HR:1.33, 95%CI: 1.11,1.61). Compared to newborns with 40 weeks of gestation, those with 37, 38 and 41 weeks gestation had a higher risk of ENM (HR:1.98, 95%CI:1.58,2.49; HR:1.54, 95%CI: 1.25,1.90; HR:1.36, 95%CI: 1.04,1.79) respectively. Neonates whose mothers had inadequate and intermediate Kotelchuck index had 19% (HR: 1.19, 95%CI: 1.01,1.41) and 56% (HR: 1.56, 95%CI: 1.22,1.98) higher risk of ENM in relation to mothers with adequate Kotelchuck index. In relation to type of delivery, caesarean delivery increased the risk of ENM (HR:2.44, 95%CI: 2.12,2.80) compared to vaginal delivery. While LBW had 4 times the risk of ENM in comparison with normal weight neonates (HR:4.81, 95%CI: 4.03,5.73) (Table 3). Private hospitals Neonates whose mothers self-reported as black had a 66% increased risk of ENM compared non-black women (HR:1.66, 95%CI: 1.10,2.50). Newborns whose mothers had university studies were less likely to have ENM (HR: 0.50, 95%CI: 0.32,0.79). Reaching 39 week of gestation was a protector factor of ENM (HR:0.65, 95%CI: 0.45,0.94) compared to their reference, while neonates whose mothers had inadequate Kotelchuck index had 71% higher risk of ENM (HR: 1.71, 95%CI: 1.24,2.35) compared to adequate index. Compared to primiparous women, neonates whose mothers had two or more children had higher risk of ENM (HR: 1.51, 95%CI:1.09,2.08). For newborns, LBW was a risk factor for ENM (HR: 11.05, 95%CI: 8.59,14.19) and being born female was protective for ENM compared to boys (HR:0.74, 95%CI:0.60,0.92) (Table 3). Late neonatal mortality Public hospitals Neonates whose mothers were under 20 had a higher risk of LNM (HR:1.46, 95%CI: 1.15,1.86) compared to neonates with mothers aged 20 to 34. The higher risk of neonatal mortality after the seventh day of life continues in newborns with 37 and 38 weeks of gestation (HR:2.19, 95%CI: 1.66,2.89; HR:1.75, 95%CI: 1.36,2.25) respectively. Regarding antenatal care, neonates whose mothers had intermediate Kotelchuck index had 61% higher risk of LNM (HR:1.61, 95%CI: 1.21, 2.15) compared to mothers with adequate index. Parity appears to be a risk factor for LNM (HR:1.27, 95%CI: 1.00,1.61), while caesarean and LBW continue to be risk factors for LNM (HR: 1.86, 95%CI: 1.57,2.20; HR: 3.86, 95%CI: 3.07,4.85) (Table 4). Private hospitals Neonates whose mothers were highly educated continued to be at lower risk of LNM (HR:0.35, 95%CI: 0.20,0.59) compared to neonates with mothers with elementary school. Neonates with 37 weeks of gestation were at greater risk of LNM compared to neonates with 40 weeks of gestation (HR:1.94, 95%CI:1.18,3.18). LBW was associated with higher risk of LNM (HR:5.65, 95%CI: 3.98,7.99). Similarly, as observed in ENM, female neonates in private hospitals had lower risk of LNM compared to male neonates (HR: 0.69, 95%CI: 0.53,0.89) (Table 4). DISCUSSION Out of the total number of neonatal deaths, 60% were early neonatal deaths. Our results suggest that there was a significant difference between early and late neonatal mortality by type of hospital. In public hospitals, there are a greater number of risk factors associated with ENM, and these are mostly related to characteristics of prenatal care and childbirth, while there are no protective factors. In addition, in public hospitals the same risks factors of neonatal mortality remained after the first 6 days of birth. The mother's age ≥ 35 years was associated with ENM, while those aged ≤ 19 were associated with LNM only in public hospitals, reflecting the social vulnerability of this population. Worldwide, advanced maternal age (AMA) defined as the age to childbearing at 35 years or older, is increasing. Independent and economically stable women are more likely to be AMA, especially with the precariousness of labor relations, maternity leave, job stability and others working mothers benefits. There are previous reports that women with AMA have more maternal complications and adverse neonatal events such as LBW and perinatal mortality compared to younger women [ 13 ]. However, in low- and middle-income countries (LMICs), mothers less than 25 years and those 30 years or older are at greater risk of NM [ 14 ]. Previous studies have shown that the mother's education is a strong predictor of infant mortality [ 15 ], thus neonatal mortality could be highly influenced by it. In this study, this protective association occurred only in mothers attended by private hospitals, where 59% of them had a university education. On the other hand, only 8% of the mothers attended by public hospitals had university studies. These data reflect the overall better economic status of women attending the private sector. Having two or more children was a risk factor for MN in both types of hospitals. High parity in itself seems not to be related to MN, however it may reflect a high vulnerability to socioeconomic and environmental determinants of the mothers [ 16 ]. According to the characteristics of the pregnancy, gestational weeks 37, 38 and 41 were associated with greater NM in women attended in public hospitals, while in the private sector, only the 37th gestational week increased the risk of LNM. Our results are similar to US national data in which both gestational weeks (37 and 38) indicate risk of NM compared to 40 weeks gestation, regardless of ethnic/racial groups [ 17 ]. This association was also founded in the Brazilian live-births cohort in the “Birth in Brazil” [ 18 ], while other study points to a slight increase in the risk of perinatal mortality from week 41 of gestation onwards [ 19 ]. In addition, women with 39 weeks of gestation attended in private hospitals had a 35% lower risk of ENM. These women were mostly white (70%), aged 20–34 (72%) and had a cesarean delivery (83%). Inadequate prenatal care was associated with an increased risk of ENM, consistent with the findings in cohort studies, conducted in the United States and Brazil [ 18 , 20 – 21 ]. In one of these studies, this association was mediated by birth weight and gestational age [ 20 ]; however, in our results this association was maintained despite being adjusted for both variables. Additionally, intermediate prenatal care also increased the risk of NM in public hospitals. The evidence on the association between caesarean delivery and NM has been contradictory. Some findings have shown an inverse relationship between the rate of caesarean sections and NM [ 22 ], whereas others suggest that caesarean section increased the risk of NM [ 11 , 23 ]. Cesarean section has intrinsic risks, but in cases where vaginal birth is unfeasible or insecure for baby, cesarean section is well indicated and reduces neonatal mortality [ 24 ]. However, data shows that Brazil performs unnecessary caesareans in low-risk pregnancies among women, that are more likely to be highly educated, adding a possible iatrogenic risk [ 25 – 26 ]. Our results show that cesarean section increased the risk of NM only in women treated in public hospitals. Care does not seem to be the same for all women in the private sector, since newborns of black women had a 66% higher risk of ENM. It was only possible to observe this difference because in this study we chose to compare the outcomes of children of black women with those of non-black women. It is more usual in Brazil to combine brown (“parda”) and black (“preta”) women in a category called “negra”, although brown women can sometimes be closer to black, or to white women. In this study, newborn of brown women had outcomes more similar to newborns of white women, and newborn of black women had a statistically higher risk for early neonatal death in private hospitals. We know that our choice limits the comparison with other studies, but otherwise we would not be able to see this statistical difference. LBW is the highest risk factor for neonatal mortality even in term babies [ 27 – 28 ]. Our results show that LBW was a strong predictor of NM in both types of hospitals. However, the risk of ENM was greater in private hospitals rather than public hospitals. Some of the characteristics that could partially explain the excess risk of ENM in in the private sector are that the majority were born by cesarean section (87%) and 77% were born at 37 and 38 weeks of gestation. In comparison with those neonatal deaths in the public sector, 58% had a cesarean section and 62% were born at 37 to 38 weeks of gestation (data not shown). Male neonates had a higher risk of NM in relation to female neonates, similar to studies in lower-middle income and high-income countries [ 1 , 29 – 31 ]. The differences in mortality by sex could be related to the hypothesis of male disadvantage. Male newborns are at greater risk of intrauterine growth restriction, prematurity, asphyxia and respiratory distress [ 32 ]. This could be because testosterone suppresses the immune system, especially in very low birth weight (VLBW) and preterm neonates [ 31 ]. On the other hand, the levels of estradiol hormone present in female newborns would be associated with a better immune response in relation to male ones [ 33 ]. This study has some limitations. First, through the linkage of the data, it was not possible to obtain more socioeconomic information of the mother, such as income and/or wealth index, and mother's socioeconomic variables that could better explain our findings. However, we obtained the schooling grade that we used as a proxy for socioeconomic level, finding similar results with others middle- and high-income countries. We use type of hospital (public and private sector), which is also a proxy of income and wealth. This information is possible because in the city of São Paulo every maternity bed is classified by source of funding, complementing the data in the SINASC. Second, although we had the information of source of funding, we did not consider the complexity of the hospital (number of beds in the neonatal intensive care, specialists in high-risk deliveries, etc), whether or not the private hospitals had a connection with the SUS, and the health insurance of the mothers at the time of delivery. Despite the lack of these data, our results are similar to a study carried out in the city of Sao Paulo, which found that neonatal mortality in SUS hospitals was higher than in private hospitals and found no differences in neonatal mortality according to hospital complexity [ 34 ]. Conclusion This study shows that many of the risk factors for early or late neonatal mortality that apply to the richest population do not apply to the poorest population, and vice versa, since women who use the private sector and the public sector are very different in their socio-demographic characteristics and exposure to health determinants. Black skin color was a risk factor only in the private sector, indicating the need to study different patterns of institutional racism in maternal and child health care. Cesarean section was a risk factor only in the public sector, possibly reflecting its use in women at higher risk, compared to indiscriminate use in the private sector. Despite serving a richer population, in the private sector the majority of babies were born before reaching full term, reflecting the predominance of antepartum cesarean sections. Abbreviations NM: Neonatal Mortality; ENM: Early Neonatal Mortality; LNM : Later Neonatal Mortality; LBW: Low Birth Weight; SUS: Sistema Único de Saúde; DN: Live Birth Certificates; SINASC: Brazilian Live Births Information System; CEinfo: Epidemiology and Information Coordination; DO: Death Certificate; SIM: Brazilian Mortality System; ICD-10: Classification of Diseases and Related Health Problems; IBGE: Brazilian Institute of Geography and Statistics; MKI: Modified Kotelchuck Index; PNC: Prenatal Care ; AMA: Advanced Maternal Age; LMICs: low- and middle-income countries; VLBW: Very Low Birth Weight . Declarations Acknowledgments Not applicable Authors’ contributions ARL conceptualized and designed the study, drafted portions of the initial manuscript, and conducted the data analyses. ERJ and MRQ drafted portions of the manuscript, cleaned and managed data. CSGD coordinated and supervised data collection and drafted portions of the manuscript. All authors contributed to the analysis, critically reviewed and revised the manuscript, approved the final manuscript as submitted, and agreed to be accountable for all aspects of the work. Funding This study form part of the "Birthing Data” project, funded by Bill and Melinda Gates Foundation (Grant ID OPP1201939 and ID INV-027961) and National Council for Scientific and Technological Development (CNPq) (N° 443775/2018-4 and 445847/2020-4). Data availability The datasets generated and/or analyzed during the current study are not publicly available. The data cannot be shared publicly because of the due to ethical reasons. However, data could be available from the study coordinator (CSGD) upon reasonable request. 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Cad Saúde Pública. 2010; 26(1): 123-134. Tables Table 1. Baseline characteristics of the mother, pregnancy, delivery, and newborn, Municipality of Sao Paulo. By type of hospital Overall Public Private Characteristics n (%) n (%) n (%) p-value* 1,042,772 564,398 (54.1%) 478,374 (45.9%) Sociodemographic variables Mother’s age <0.001 ≤19 120,034 (11.5) 103,956 (18.4) 16,078 (3.4) 20-34 727,826 (69.8) 390,934 (69.3) 336,892 (70.4) ≥35 194,912 (18.7) 69,508 (12.3) 125,404 (26.2) Skin color <0.001 White 551,661 (52.9) 215,242 (38.2) 336,419 (70.4) Black 68,654 (6.6) 49,815 (8.8) 18,839 (3.9) Brown 403,991 (38.8) 291,085 (51.6) 112,906 (23.6) Oriental 13,399 (1.3) 3,887 (0.7) 9,512 (2.0) Indigenous 4,359 (0.4) 4,050 (0.7) 309 (0.1) Mother schooling Elementary 178,304 (17.1) 161,802 (28.7) 16,502 (3.4) <0.001 High 532,710 (51.1) 354,535 (62.9) 178,175 (37.3) University 330,644 (31.7) 47,314 (8.4) 283,330 (59.3) Live with partner Yes 591,707 (56.8) 254,645 (45.2) 337,062 (70.5) <0.001 No 449,592 (43.2) 308,905 (54.8) 140,687 (29.5) Pregnancy and delivery variables Gestational weeks <0.001 37 120,511 (11.5) 52,635 (9.3) 67,876 (14.2) 38 286,878 (27.5) 112,577 (20.0) 174,301 (36.4) 39 345,654 (33.2) 188,018 (33.3) 157,636 (32.9) 40 224,617 (21.5) 158,962 (28.2) 65,655 (13.7) 41 65,112 (6.2) 52,206 (9.2) 12,906 (2.7) Kotelchuk Index <0.001 Inadequate 162,151 (15.6) 125,735 (22.4) 36,416 (7.6) Intermediate 49,722 (4.8) 34,761 (6.2) 14,961 (3.1) Adequate 767,773 (74.0) 366,162 (65.3) 401,611 (84.2) Adequate Plus 57,852 (5.6) 33,787 (6.0) 24,065 (5.0) Parity <0.001 0 510,897 (48.9) 245,093 (43.5) 265,804 (55.6) 1 334,370 (32.1) 171,197 (30.3) 163,173 (34.2) 2 or more 196,634 (19.0) 147,752 (26.2) 48,882 (10.2) Type of birth <0.001 Vaginal 451,569 (43.3) 377,169 (66.8) 74,400 (15.5) Caesarean 591,163 (56.7) 187,209 (33.2) 403,954 (84.5) Newborn variables Birth weight <0.001 ≥2500 1,002,110 (96.1) 540,326 (95.7) 461,784 (96.5) <2500 40,662 (3.9) 24,072 (4.3) 16,590 (3.5) Sex 0.66 Boys 531,471 (51.0) 287,761 (51.0) 243,710 (51.0) Girls 511,234 (49.0) 276,583 (49.0) 234,651 (49.0) *p-value of chi-squared test. Table 2. Rates of neonatal mortality per 1000 births at term by type of hospital, Brazil. Mortality Public hospital (n=564,398) Private hospital (n=478,374) n Rate N Rate Total p-value* Early neonatal 861 1.5 352 0.7 1,213 <0.001 Later neonatal 571 1.0 239 0.5 810 <0.001 Total neonatal mortality 1,432 2.5 591 1.2 2,023 <0.001 *p-value of Chi-square test. Table 3. Early neonatal mortality at term by type of hospital. Unadjusted Hazard Ratios Adjusted Hazard Ratios FT FT Public Private Public Private HR (95%CI) HR (95%CI) HR (95%CI) HR (95%CI) Mother’s age ≤19 0.87 (0.72,1.05) 1.45 (0.87,2.41) 0.90 (0.73,1.11) 1.11 (0.65,1.89) 20-34 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) ≥35 1.55 (1.30,1.86) 1.22 (0.97,1.54) 1.33 (1.11,1.61) 1.15 (0.89,1.46) Skin color Non-black 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Black 1.26 (1.02,1.57) 1.94 (1.30,2.89) 1.16 (0.94,1.44) 1.66 (1.10,2.50) Mother schooling Elementary 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) High 0.92 (0.79,1.07) 0.63 (0.41,0.96) 0.94 (0.80,1.10) 0.76 (0.49,1.18) University 1.12 (0.87,1.43) 0.36 (0.24,0.55) 0.95 (0.73,1.24) 0.50 (0.32,0.79) Live with partner Yes 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) No 1.21 (1.06,1.38) 0.89 (0.71,1.12) 1.12 (0.98,1.29) 1.06 (0.84,1.35) Gestational weeks 37 3.29 (2.66,4.08) 2.11 (1.48,2.99) 1.98 (1.58,2.49) 1.09 (0.75,1.59) 38 1.85 (1.51,2.27) 1.01 (0.72,1.43) 1.54 (1.25,1.90) 0.84 (0.60,1.20) 39 1.17 (0.96,1.43) 0.70 (0.49,1.02) 1.13 (0.93,1.39) 0.65 (0.45,0.94) 40 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 41 1.41 (1.07,1.85) 1.36 (0.72,2.56) 1.36 (1.04,1.79) 1.21 (0.64,2.28) Kotelchuk Index Inadequate 1.18 (1.00,1.38) 2.11 (1.55,2.87) 1.19 (1.01,1.41) 1.71 (1.24,2.35) Intermediate 1.59 (1.25,2.02) 1.88 (1.17,3.04) 1.56 (1.22,1.98) 1.46 (0.89,2.37 ) Adequate 1.26 (0.96,1.64) 1.49 (0.98,2.29) 1.23 (0.94,1.61) 1.21 (0.79,1.87) Adequate Plus 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Parity 0 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1 0.97 (0.83,1.14) 1.07 (0.84,1.35) 0.91 (0.77,1.08) 1.06 (0.83,1.36) 2 or more 1.03 (0.87,1.21) 1.81 (1.35,2.43) 0.85 (0.70,1.03) 1.51 (1.09,2.08) Delivery Vaginal 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Caesarean 2.71 (2.37,3.11) 1.22 (0.89,1.67) 2.44 (2.12,2.80) 1.20 (0.88,1.65) Birth weight ≥2500 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) <2500 6.72 (5.74,7.88) 12.18 (9.70,15.29) 4.81 (4.03,5.73) 11.05 (8.59,14.19) Gender Boys 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Girls 0.95 (0.83,1.08) 0.84 (0.67,1.03) 0.89 (0.78,1.02) 0.74 (0.60,0.92) Table 4. Late neonatal mortality at term by type of hospital. Unadjusted Hazard Ratios Adjusted Hazard Ratios FT FT Public Private Public Private HR (95%CI) HR (95%CI) HR (95%CI) HR (95%CI) Mother’s age ≤19 1.22 (0.99,1.50) 1.15 (0.58,2.25) 1.46 (1.15,1.86) 0.85 (0.42,1.72) 20-34 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) ≥35 1.45 (1.15,1.82) 1.08 (0.81,1.44) 1.17 (0.92,1.49) 1.07 (0.79,1.44) Skin color Non-black 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Black 1.14 (0.87,1.50) 1.41 (0.80,2.46) 1.09 (0.83,1.44) 1.26 (0.72,2.21) Mother schooling Elementary 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) High 0.90 (0.75,1.08) 0.63 (0.38,1.04) 1.01 (0.83,1.22) 0.66 (0.40,1.10) University 1.08 (0.80,1.47) 0.32 (0.19,0.52) 1.16 (0.84,1.60) 0.35 (0.20,0.59) Live with partner Yes 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) No 1.20 (1.02,1.41) 0.81 (0.61,1.06) 1.18 (0.99,1.39) 0.94 (0.70,1.24) Gestational weeks 37 3.25 (2.50,4.23) 2.74 (1.70,4.40) 2.19 (1.66,2.89) 1.94 (1.18,3.18) 38 2.00 (1.57,2.56) 1.38 (0.87,2.18) 1.75 (1.36,2.25) 1.29 (0.81,1.84) 39 1.17 (0.92,1.50) 1.10 (0.68,1.78) 1.16 (0.90,1.49) 1.09 (0.62,2.13) 40 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 41 1.24 (0.87,1.76) 1.33 (0.54,3.26) 1.22 (0.86,1.74) 1.02 (0.22,1.02) Kotelchuk Index Inadequate 1.19 (0.97,1.45) 1.63 (1.09,2.42) 1.12 (0.92,1.38) 1.22 (0.80,1.84) Intermediate 1.77 (1.33,2.35) 1.69 (0.95,3.04) 1.61 (1.21,2.15) 1.15 (0.62,2.13) Adequate 1.24 (0.88,1.73) 0.61 (0.29,1.31) 1.15 (0.82,1.61) 0.48 (0.22,1.02) Adequate Plus 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Parity 0 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1 0.93 (0.76,1.14) 1.14 (0.86,1.50) 1.02 (0.82,1.27) 1.10 (0.82,1.47) 2 or more 1.22 (1.00,1.48) 1.69 (1.17,2.45) 1.27 (1.00,1.61) 1.34 (0.89,1.99) Delivery Vaginal 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Caesarean 1.93 (1.64,2.27) 1.11 (0.77,1.60) 1.86 (1.57,2.20) 1.11 (0.77,1.61) Birth weight ≥2500 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) <2500 5.40 (4.38,6.65) 6.50 (4.70,8.99) 3.86 (3.07,4.85) 5.65 (3.98,7.99) Gender Boys 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Girls 0.89 (0.76,1.05) 0.75 (0.57,0.96) 0.87 (0.74,1.03 ) 0.69 (0.53,0.89) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4477653","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310429496,"identity":"dc68dbd3-3252-4576-aa00-91c77f026967","order_by":0,"name":"Alejandra Andrea Roman Lay","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBADGX4QyQNGMMCOXwuPZAOKlgQgZiagxeAAA7IVeLTwt599+OhGzR0e4+OHH354U7FNhr+9/QEz7w87Bv5m7FokzqQbG+cce8ZjdibNWHLOmds8EmfOGDDzJCQzSBzG4aIDaWzSOWyHecwO5DBI87bd5jGQyGEAamFmMMDhMPnzz4Ba/h3mMe5/w/yb9x9IS/oDoJZ6nFoMbgBtyW07DDKcTZq3AaQlAeSwwzi1GN54xmyc23eYR+LGMzPLOccgfjk4J+04Dy6/yJ1PY3yc8+2wHH9/8uMbb2pu2wND7OGDNzbVcvztDTj8jzVMGFCSwSgYBaNgFIwCUgEAFkRVaB6ShAgAAAAASUVORK5CYII=","orcid":"","institution":"University of Tarapacá","correspondingAuthor":true,"prefix":"","firstName":"Alejandra","middleName":"Andrea Roman","lastName":"Lay","suffix":""},{"id":310429497,"identity":"354f0d37-109d-4339-9216-87f6bca1cd5b","order_by":1,"name":"Maria Elizangela Ramos Junqueira","email":"","orcid":"","institution":"State University of Bahia","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Elizangela Ramos","lastName":"Junqueira","suffix":""},{"id":310429498,"identity":"57f5766e-230c-4d23-a2de-eb1fc450cceb","order_by":2,"name":"Marcel Reis Queiroz","email":"","orcid":"","institution":"Nove de Julho University","correspondingAuthor":false,"prefix":"","firstName":"Marcel","middleName":"Reis","lastName":"Queiroz","suffix":""},{"id":310429499,"identity":"d1aba351-8cef-428e-bc99-bbe662c2525b","order_by":3,"name":"Carmen Simone Grilo Diniz","email":"","orcid":"","institution":"University of Sao Paulo","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"Simone Grilo","lastName":"Diniz","suffix":""}],"badges":[],"createdAt":"2024-05-25 17:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4477653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4477653/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58760071,"identity":"d293970e-3b14-476a-bf65-520a9ffd08e9","added_by":"auto","created_at":"2024-06-20 18:47:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14312,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of newborn cohort between 2012 and 2017 in the Municipality of São Paulo, Brazil.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4477653/v1/1a8293c9e09669aa59aa5f9b.png"},{"id":68379082,"identity":"7e590125-908e-4c86-b705-24cb70082a5e","added_by":"auto","created_at":"2024-11-06 16:01:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1113634,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4477653/v1/ab97464a-a67d-400c-9f6a-700110c5a7f6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early and late neonatal mortality in term newborns: Survival differences according to public and private hospitals in Brazil","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eNeonatal mortality (NM) can be subdivided in early neonatal mortality (ENM) and late neonatal mortality (LNM). ENM refers to the number of deaths occurring during the first 6 days of life, while LNM are those that occur from day 7 to 27 of birth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Brazil, NM has been decreasing from 2000 to 2020, from 16.7 deaths/1000 live births (lb) to 8.8 deaths/1000 lb respectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, it continues to be a public health challenge.\u003c/p\u003e \u003cp\u003eNM is influenced by maternal socioeconomic factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], prenatal care [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], type of delivery (vaginal or caesarean) and characteristics of the place of birth; e.g. some studies suggest that the risk of NM is higher in public hospitals than in private ones [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Public hospitals in Brazil belong to the unified health system (Sistema \u0026Uacute;nico de Sa\u0026uacute;de, SUS) that promotes universal health coverage. Health inequalities increased with decreased public funding and with the unmet health needs due to the political and economic crises suffered since 2016 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These reflect the situation prior to the COVID-19 pandemic, which collapsed even more the Brazilian health system [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA better understanding of NM risk factors that are sensitive to socioeconomic changes and health inequalities in Brazil, would allow improvements to public prevention of NM and a reduction of regional inequalities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to identify the risk factors for early and late neonatal mortality with a focus on determinants related to childbirth in public and private hospitals, in newborns at term in the city of S\u0026atilde;o Paulo, Brazil, including socio-demographic, obstetric and health care characteristics.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population and data collection\u003c/h2\u003e \u003cp\u003eThis is a cohort study of newborn in the city of S\u0026atilde;o Paulo with follow-up from January 1, 2012 to December 31, 2017 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe use prenatal and socioeconomic data of live births certificates (DN) from the Brazilian live births information system (SINASC) managed by the Epidemiology and Information Coordination (CEInfo) of the S\u0026atilde;o Paulo Municipal Health Department, linked to death records contained in the Death Certificate (DO) of the Brazilian Mortality System (SIM). We obtain data from babies born between January 1, 2012, and December 31, 2017, followed until December 31, 2018. The individual records of the two sets of data were deterministically and probabilistically paired and linked through the variables DN number, child's sex and date of birth. In total, 13,347 pairs were found. More details about the linkage process have been previously described by Queiroz et al [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBetween 2012 and 2017, 1,202,843 births were registered in the city of S\u0026atilde;o Paulo. Of which we excluded 4,446 women without information on gestational age in weeks, totaling 1,198,397 women. Since our focus were the term babies, we excluded preterm pregnancies (gestational age of less than 37 weeks, n\u0026thinsp;=\u0026thinsp;133,478) and post term (over 42 weeks, n\u0026thinsp;=\u0026thinsp;15,757), 7 newborns without information on the day of birth and 6,383 missing data on type of hospital. Thus, the total sample for analysis was 1,042,772 women (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eThe outcome was NM, considered as all deaths from birth to 27 complete days of birth. This was divided into ENM (0\u0026ndash;6 days) and LNM (7\u0026ndash;27). All-cause mortality was obtained through linkage between SINASC and SIM. The classification of deaths was obtained according to the underlying cause of death, categorized by the International Classification of Diseases and Related Health Problems (ICD-10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePredictors\u003c/h2\u003e \u003cp\u003eThe characteristics of the mothers included were maternal age in years (\u0026le;\u0026thinsp;19, 20\u0026ndash;34, \u0026ge;\u0026thinsp;35), schooling (elementary, high, university), lives with a partner (yes, no) and skin color (black, non-black). In Brazil, the racial classification system follows the standard proposed by IBGE (Brazilian Institute of Geography and Statistics), classifying people as white, brown, black, oriental and indigenous people. In this study, we categorized race as black and non-black since studies indicate that in Brazil early neonatal mortality is highest for babies born to black women [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Non-black included white, oriental, indigenous and brown people. Other variables related to pregnancy and delivery were gestational age (37,38,39,40,41 completed weeks), parity (0, 1, 2 or more) and type of delivery (vaginal or caesarean). We also included the evaluation of adequate prenatal control through the modified kotelchuck index (MKI). This index is based on information about the month in which prenatal care (PNC) begins and the number of prenatal visits during the pregnancy. MKI classified women into: without prenatal visits, inadequate (onset of PNC\u0026thinsp;\u0026gt;\u0026thinsp;3 months or onset of PNC\u0026thinsp;\u0026lt;\u0026thinsp;4 months but \u0026lt;\u0026thinsp;3 prenatal visits), intermediate (onset of PNC\u0026thinsp;\u0026lt;\u0026thinsp;4 months and number of prenatal visits between 3 to 5), adequate (onset of PNC\u0026thinsp;\u0026lt;\u0026thinsp;4 months and number of prenatal visits equal to 6) and overadequate (onset of PNC\u0026thinsp;\u0026lt;\u0026thinsp;4 months and number of prenatal visits greater than 6). Only 0.7% of the women did not have prenatal visits (n\u0026thinsp;=\u0026thinsp;6,768). Thus, to ensure proper statistical analysis, these women were categorized as inadequate MKI. The newborn characteristics were low birth weight (LBW) (\u0026lt;\u0026thinsp;2500g versus \u0026ge;\u0026thinsp;2500g) and sex assigned at birth (male or female).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe differences between the characteristics of the newborns by type of hospital and early, late and total neonatal mortality rates were calculated using the chi-square test.\u003c/p\u003e \u003cp\u003eCox regression models were performed to estimate the association between the independent variables and early and late neonatal mortality, stratified by public and private care. The results were expressed in Hazard ratios (HRs) with a confidence interval of 95% (95% CI). All analyses were performed using STATA 15 (Stata Corp., College Station, Texas, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOut of the total term births, 72% reached up to 39 weeks of gestation. The majority were mothers between 20 and 34 years old (70%), non-black (93%) and primiparous (49%), and 74% of them had Kotelchuck index adequately. In relation to delivery, 57% were caesarean and 43% vaginal, while 54% were attended in the public hospital and 46% in the private hospital.\u0026nbsp;There were significant differences between all baseline characteristics by type of hospital, except for sex of the baby (Table 1). Compared with private sector, all markers associated with social vulnerability were present in public sector: more adolescents, more black women, mother\u0026apos;s lower education, higher number of mothers without partner and inadequate antenatal care. Additionally, there was more multiparity and low birthweight, and less cesarean sections.\u003c/p\u003e\n\u003cp\u003eBetween the years 2012 to 2017 there were a total of 2,023 neonatal deaths. Of which 1,213 corresponded to early neonatal deaths and 810 late neonatal deaths. There were significant differences in neonatal mortality according to sector. Mortality rates for both ENM and LNM in the public sector (2.5 *1000 births) were double those in the private sector (1.2*1000 births) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEarly neonatal mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublic hospitals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeonates whose mothers were \u0026ge;35 years had increased risk of ENM compared with mothers aged 20 to 34 (HR:1.33, 95%CI: 1.11,1.61). Compared to newborns with 40 weeks of gestation, those with 37, 38 and 41 weeks gestation had a higher risk of ENM (HR:1.98, 95%CI:1.58,2.49; HR:1.54, 95%CI: 1.25,1.90; HR:1.36, 95%CI: 1.04,1.79) respectively. Neonates whose mothers had inadequate and intermediate Kotelchuck index had 19% (HR: 1.19, 95%CI: 1.01,1.41) and 56% (HR: 1.56, 95%CI: 1.22,1.98) higher risk of ENM in relation to mothers with adequate Kotelchuck index. In relation to type of delivery, caesarean delivery increased the risk of ENM (HR:2.44, 95%CI: 2.12,2.80) compared to vaginal delivery. While LBW had 4 times the risk of ENM in comparison with normal weight neonates (HR:4.81, 95%CI: 4.03,5.73) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrivate hospitals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeonates whose mothers self-reported as black had a 66% increased risk of ENM compared non-black women (HR:1.66, 95%CI: 1.10,2.50). Newborns whose mothers had university studies were less likely to have ENM (HR: 0.50, 95%CI: 0.32,0.79). Reaching 39 week of gestation was a protector factor of ENM (HR:0.65, 95%CI: 0.45,0.94) compared to their reference, while neonates whose mothers had inadequate Kotelchuck index had 71% higher risk of ENM (HR: 1.71, 95%CI: 1.24,2.35) compared to adequate index. Compared to primiparous women, neonates whose mothers had two or more children had higher risk of ENM (HR: 1.51, 95%CI:1.09,2.08). For newborns, LBW was a risk factor for ENM (HR: 11.05, 95%CI: 8.59,14.19) and being born female was protective for ENM compared to boys (HR:0.74, 95%CI:0.60,0.92) (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLate neonatal mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublic hospitals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeonates whose mothers were under 20 had a higher risk of LNM (HR:1.46, 95%CI: 1.15,1.86) compared to neonates with mothers aged 20 to 34. The higher risk of neonatal mortality after the seventh day of life continues in newborns with 37 and 38 weeks of gestation (HR:2.19, 95%CI: 1.66,2.89; HR:1.75, 95%CI: 1.36,2.25) respectively. Regarding antenatal care, neonates whose mothers had intermediate Kotelchuck index had 61% higher risk of LNM (HR:1.61, 95%CI: 1.21, 2.15) compared to mothers with adequate index. Parity appears to be a risk factor for LNM (HR:1.27, 95%CI: 1.00,1.61), while caesarean and LBW continue to be risk factors for LNM (HR: 1.86, 95%CI: 1.57,2.20; HR: 3.86, 95%CI: 3.07,4.85) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrivate hospitals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeonates whose mothers were highly educated continued to be at lower risk of LNM (HR:0.35, 95%CI: 0.20,0.59) compared to neonates with mothers with elementary school. Neonates with 37 weeks of gestation were at greater risk of LNM compared to neonates with 40 weeks of gestation (HR:1.94, 95%CI:1.18,3.18).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLBW was associated with higher risk of LNM (HR:5.65, 95%CI: 3.98,7.99). Similarly, as observed in ENM, female neonates in private hospitals had lower risk of LNM compared to male neonates (HR: 0.69, 95%CI: 0.53,0.89) (Table 4).\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOut of the total number of neonatal deaths, 60% were early neonatal deaths. Our results suggest that there was a significant difference between early and late neonatal mortality by type of hospital. In public hospitals, there are a greater number of risk factors associated with ENM, and these are mostly related to characteristics of prenatal care and childbirth, while there are no protective factors. In addition, in public hospitals the same risks factors of neonatal mortality remained after the first 6 days of birth.\u003c/p\u003e \u003cp\u003eThe mother's age\u0026thinsp;\u0026ge;\u0026thinsp;35 years was associated with ENM, while those aged\u0026thinsp;\u0026le;\u0026thinsp;19 were associated with LNM only in public hospitals, reflecting the social vulnerability of this population. Worldwide, advanced maternal age (AMA) defined as the age to childbearing at 35 years or older, is increasing. Independent and economically stable women are more likely to be AMA, especially with the precariousness of labor relations, maternity leave, job stability and others working mothers benefits. There are previous reports that women with AMA have more maternal complications and adverse neonatal events such as LBW and perinatal mortality compared to younger women [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, in low- and middle-income countries (LMICs), mothers less than 25 years and those 30 years or older are at greater risk of NM [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have shown that the mother's education is a strong predictor of infant mortality [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], thus neonatal mortality could be highly influenced by it. In this study, this protective association occurred only in mothers attended by private hospitals, where 59% of them had a university education. On the other hand, only 8% of the mothers attended by public hospitals had university studies. These data reflect the overall better economic status of women attending the private sector.\u003c/p\u003e \u003cp\u003eHaving two or more children was a risk factor for MN in both types of hospitals. High parity in itself seems not to be related to MN, however it may reflect a high vulnerability to socioeconomic and environmental determinants of the mothers [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. According to the characteristics of the pregnancy, gestational weeks 37, 38 and 41 were associated with greater NM in women attended in public hospitals, while in the private sector, only the 37th gestational week increased the risk of LNM. Our results are similar to US national data in which both gestational weeks (37 and 38) indicate risk of NM compared to 40 weeks gestation, regardless of ethnic/racial groups [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This association was also founded in the Brazilian live-births cohort in the \u0026ldquo;Birth in Brazil\u0026rdquo; [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], while other study points to a slight increase in the risk of perinatal mortality from week 41 of gestation onwards [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, women with 39 weeks of gestation attended in private hospitals had a 35% lower risk of ENM. These women were mostly white (70%), aged 20\u0026ndash;34 (72%) and had a cesarean delivery (83%).\u003c/p\u003e \u003cp\u003eInadequate prenatal care was associated with an increased risk of ENM, consistent with the findings in cohort studies, conducted in the United States and Brazil [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In one of these studies, this association was mediated by birth weight and gestational age [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; however, in our results this association was maintained despite being adjusted for both variables. Additionally, intermediate prenatal care also increased the risk of NM in public hospitals.\u003c/p\u003e \u003cp\u003eThe evidence on the association between caesarean delivery and NM has been contradictory. Some findings have shown an inverse relationship between the rate of caesarean sections and NM [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], whereas others suggest that caesarean section increased the risk of NM [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Cesarean section has intrinsic risks, but in cases where vaginal birth is unfeasible or insecure for baby, cesarean section is well indicated and reduces neonatal mortality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, data shows that Brazil performs unnecessary caesareans in low-risk pregnancies among women, that are more likely to be highly educated, adding a possible iatrogenic risk [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our results show that cesarean section increased the risk of NM only in women treated in public hospitals.\u003c/p\u003e \u003cp\u003eCare does not seem to be the same for all women in the private sector, since newborns of black women had a 66% higher risk of ENM. It was only possible to observe this difference because in this study we chose to compare the outcomes of children of black women with those of non-black women. It is more usual in Brazil to combine brown (\u0026ldquo;parda\u0026rdquo;) and black (\u0026ldquo;preta\u0026rdquo;) women in a category called \u0026ldquo;negra\u0026rdquo;, although brown women can sometimes be closer to black, or to white women. In this study, newborn of brown women had outcomes more similar to newborns of white women, and newborn of black women had a statistically higher risk for early neonatal death in private hospitals. We know that our choice limits the comparison with other studies, but otherwise we would not be able to see this statistical difference.\u003c/p\u003e \u003cp\u003eLBW is the highest risk factor for neonatal mortality even in term babies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our results show that LBW was a strong predictor of NM in both types of hospitals. However, the risk of ENM was greater in private hospitals rather than public hospitals. Some of the characteristics that could partially explain the excess risk of ENM in in the private sector are that the majority were born by cesarean section (87%) and 77% were born at 37 and 38 weeks of gestation. In comparison with those neonatal deaths in the public sector, 58% had a cesarean section and 62% were born at 37 to 38 weeks of gestation (data not shown).\u003c/p\u003e \u003cp\u003eMale neonates had a higher risk of NM in relation to female neonates, similar to studies in lower-middle income and high-income countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The differences in mortality by sex could be related to the hypothesis of male disadvantage. Male newborns are at greater risk of intrauterine growth restriction, prematurity, asphyxia and respiratory distress [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This could be because testosterone suppresses the immune system, especially in very low birth weight (VLBW) and preterm neonates [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. On the other hand, the levels of estradiol hormone present in female newborns would be associated with a better immune response in relation to male ones [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, through the linkage of the data, it was not possible to obtain more socioeconomic information of the mother, such as income and/or wealth index, and mother's socioeconomic variables that could better explain our findings. However, we obtained the schooling grade that we used as a proxy for socioeconomic level, finding similar results with others middle- and high-income countries. We use type of hospital (public and private sector), which is also a proxy of income and wealth. This information is possible because in the city of S\u0026atilde;o Paulo every maternity bed is classified by source of funding, complementing the data in the SINASC. Second, although we had the information of source of funding, we did not consider the complexity of the hospital (number of beds in the neonatal intensive care, specialists in high-risk deliveries, etc), whether or not the private hospitals had a connection with the SUS, and the health insurance of the mothers at the time of delivery. Despite the lack of these data, our results are similar to a study carried out in the city of Sao Paulo, which found that neonatal mortality in SUS hospitals was higher than in private hospitals and found no differences in neonatal mortality according to hospital complexity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study shows that many of the risk factors for early or late neonatal mortality that apply to the richest population do not apply to the poorest population, and vice versa, since women who use the private sector and the public sector are very different in their socio-demographic characteristics and exposure to health determinants.\u003c/p\u003e \u003cp\u003eBlack skin color was a risk factor only in the private sector, indicating the need to study different patterns of institutional racism in maternal and child health care.\u003c/p\u003e \u003cp\u003eCesarean section was a risk factor only in the public sector, possibly reflecting its use in women at higher risk, compared to indiscriminate use in the private sector. Despite serving a richer population, in the private sector the majority of babies were born before reaching full term, reflecting the predominance of antepartum cesarean sections.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eNM:\u0026nbsp;\u003c/strong\u003eNeonatal Mortality; \u003cstrong\u003eENM:\u0026nbsp;\u003c/strong\u003eEarly Neonatal Mortality;\u0026nbsp;\u003cstrong\u003eLNM\u003c/strong\u003e: Later Neonatal Mortality; \u003cstrong\u003eLBW:\u003c/strong\u003e Low Birth Weight; \u003cstrong\u003eSUS:\u003c/strong\u003e Sistema \u0026Uacute;nico de Sa\u0026uacute;de; \u003cstrong\u003eDN:\u003c/strong\u003e Live Birth Certificates; \u003cstrong\u003eSINASC:\u003c/strong\u003e Brazilian Live Births Information System; \u003cstrong\u003eCEinfo:\u003c/strong\u003e Epidemiology and Information Coordination; \u003cstrong\u003eDO:\u003c/strong\u003e Death Certificate; \u003cstrong\u003eSIM:\u003c/strong\u003e Brazilian Mortality System; \u003cstrong\u003eICD-10:\u0026nbsp;\u003c/strong\u003eClassification of Diseases and Related Health Problems; \u003cstrong\u003eIBGE:\u003c/strong\u003e Brazilian Institute of Geography and Statistics; \u003cstrong\u003eMKI:\u003c/strong\u003e Modified Kotelchuck Index; \u003cstrong\u003ePNC:\u0026nbsp;\u003c/strong\u003ePrenatal Care\u003cstrong\u003e; AMA:\u0026nbsp;\u003c/strong\u003eAdvanced Maternal Age;\u003cstrong\u003e\u0026nbsp;LMICs:\u0026nbsp;\u003c/strong\u003elow- and middle-income countries; \u003cstrong\u003eVLBW:\u003c/strong\u003e Very Low Birth Weight\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eARL conceptualized and designed the study, drafted portions of the initial manuscript, and conducted the data analyses. ERJ and MRQ drafted portions of the manuscript, cleaned and managed data.\u003c/p\u003e\n\u003cp\u003eCSGD coordinated and supervised data collection and drafted portions of the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the analysis, critically reviewed and revised the manuscript, approved the final manuscript as submitted, and agreed to be accountable for all aspects of the work.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study form part of the \u0026quot;Birthing Data\u0026rdquo; project, funded by Bill and Melinda Gates Foundation (Grant\u0026nbsp;ID OPP1201939 and\u0026nbsp;ID INV-027961)\u0026nbsp;and National Council for Scientific and Technological Development (CNPq) (N\u0026deg;\u0026nbsp;443775/2018-4 and\u0026nbsp;445847/2020-4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available. The data cannot be shared publicly because of the due to ethical reasons. However, data could be available from the study coordinator (CSGD) upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was approved by the research ethics committee of School of Public Health, University of S\u0026atilde;o Paulo (N\u0026deg; 98163018.2.0000.5421).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eAghai ZH, Goudar SS, Patel A, et al. 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Pesquisa Nascer no Brasil: perfil da mortalidade neonatal e avalia\u0026ccedil;\u0026atilde;o da assist\u0026ecirc;ncia \u0026agrave; gestante e ao rec\u0026eacute;m-nascido. Cad De Sa\u0026uacute;de P\u0026uacute;blica. 2014; 30 (Suppl 1): S192\u0026ndash;S207.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIngemarsson I, K\u0026auml;ll\u0026eacute;n K. Stillbirths and rate of neonatal deaths in 76,761 postterm pregnancies in Sweden, 1982-1991: a register study. Acta Obstet Gynecol Scand. 1997;76(7):658-62.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eChen XK, Wen SW, Yang Q, Walker MC. Adequacy of prenatal care and neonatal mortality in infants born to mothers with and without antenatal high-risk conditions. Aust N Z J Obstet Gynaecol. 2007; 47(2):122-7.\u003c/li\u003e\n \u003cli\u003ePartridge S, Balayla J, Holcroft CA, Abenhaim HA. Inadequate prenatal care utilization and risks of infant mortality and poor birth outcome: a retrospective analysis of 28,729,765 U.S. deliveries over 8 years.\u0026nbsp;Am J Perinatol. 2012;29(10):787-93.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMolina G, Weiser TG, Lipsitz SR, et al.\u0026nbsp;Relationship Between Cesarean Delivery Rate and Maternal and Neonatal Mortality. JAMA. 2015; 314(21):2263\u0026ndash;2270.\u003c/li\u003e\n \u003cli\u003eWoday Tadesse A, Mekuria Negussie Y, Aychiluhm SB. Neonatal mortality and its associated factors among neonates admitted at public hospitals, pastoral region, Ethiopia: A health facility based study. PLoS One. 2021;16(3):e0242481.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBetran AP, Torloni MR, Zhang JJ, G\u0026uuml;lmezoglu AM, WHO Working Group on Caesarean Section. WHO Statement on Caesarean Section Rates. BJOG. 2016; 123(5):667-70.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBoerma T, Ronsmans C, Melesse DY, et al. Global epidemiology of use of and disparities in caesarean sections. Lancet. 2018; 392(10155):1341-1348.\u003c/li\u003e\n \u003cli\u003eBrasil. Minist\u0026eacute;rio da Sa\u0026uacute;de. Secretaria de Vigil\u0026acirc;ncia em Sa\u0026uacute;de. Coordena\u0026ccedil;\u0026atilde;o-Geral de Informa\u0026ccedil;\u0026otilde;es e An\u0026aacute;lises Epidemiol\u0026oacute;gicas. Sistema de Informa\u0026ccedil;\u0026otilde;es sobre Nascidos Vivos \u0026ndash; SINASC. 2021. Available at:\u0026nbsp;\u003ca href=\"http://tabnet.datasus.gov.br\"\u003ehttp://tabnet.datasus.gov.br\u003c/a\u003e. Accesed May 3, 2024.\u003c/li\u003e\n \u003cli\u003eEshete A, Alemu A, Zerfu TA.\u0026nbsp;Magnitude and Risk of Dying among Low Birth Weight Neonates in Rural Ethiopia: A Community-Based Cross-Sectional Study.\u0026nbsp;Int J Pediatr. 2019; 2019:9034952.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVilanova CS, Hirakata VN, de Souza Buriol VC, Nunes M, Goldani MZ, da Silva CH.\u0026nbsp;The relationship between the different low birth weight strata of newborns with infant mortality and the influence of the main health determinants in the extreme south of Brazil.\u0026nbsp;Popul Health Metr. 2019;17(1):15.\u003c/li\u003e\n \u003cli\u003eZhao D, Zou L, Lei X, Zhang Y. Gender Differences in Infant Mortality and Neonatal Morbidity in Mixed-Gender Twins.\u0026nbsp;Sci Rep.\u0026nbsp;2017; 7: 8736.\u003c/li\u003e\n \u003cli\u003eSteen EE, K\u0026auml;ll\u0026eacute;n K, Mar\u0026scaron;\u0026aacute;l K, Norman M, Hellstr\u0026ouml;m-Westas L. Impact of sex on perinatal mortality and morbidity in twins. J Perinat Med. 2014; 42(2):225-31.\u003c/li\u003e\n \u003cli\u003eCho J, Holditch-Davis D. Effects of perinatal testosterone on infant health, mother-infant interactions, and infant development. Biol Res Nurs. 2014; 16(2):228-36.\u003c/li\u003e\n \u003cli\u003eTownsel CD, Emmer SF, Campbell WA, Hussain N. Gender Differences in Respiratory Morbidity and Mortality of Preterm Neonates.\u0026nbsp;Front Pediatr. 2017;5:6.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKlein SL, Marson AL, Scott AL, Ketner G, Glass GE. Neonatal sex steroids affect responses to Seoul virus infection in male but not female Norway rats.\u0026nbsp;Brain Behav Immun. 2002;16(6):736-46.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSilva ZP, Almeida MF, Ortiz LP, et al. Morte neonatal precoce segundo complexidade hospitalar e rede SUS e n\u0026atilde;o-SUS na Regi\u0026atilde;o Metropolitana de S\u0026atilde;o Paulo, Brasil. Cad Sa\u0026uacute;de P\u0026uacute;blica. 2010; 26(1): 123-134.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBaseline characteristics of the mother, pregnancy, delivery, and newborn, Municipality of Sao Paulo.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.58919803600654%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBy type of hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003ep-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e1,042,772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e564,398 (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e478,374 (45.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eMother\u0026rsquo;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e120,034 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e103,956 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e16,078 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e20-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e727,826 (69.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e390,934 (69.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e336,892 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e194,912 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e69,508 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e125,404 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eSkin color\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e551,661 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e215,242 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e336,419 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e68,654 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e49,815 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e18,839 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eBrown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e403,991 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e291,085 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e112,906 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eOriental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e13,399 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e3,887 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e9,512 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e4,359 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e4,050 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e309 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eMother schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eElementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e178,304 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e161,802 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e16,502 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e532,710 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e354,535 (62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e178,175 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e330,644 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e47,314 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e283,330 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eLive with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e591,707 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e254,645 (45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e337,062 (70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e449,592 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e308,905 (54.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e140,687 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePregnancy and delivery\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eGestational weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e120,511 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e52,635 (9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e67,876 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e286,878 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e112,577 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e174,301 (36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e345,654 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e188,018 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e157,636 (32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e224,617 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e158,962 (28.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e65,655 (13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e65,112 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e52,206 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e12,906 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eKotelchuk Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eInadequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e162,151 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e125,735 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e36,416 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e49,722 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e34,761 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e14,961 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eAdequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e767,773 (74.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; 366,162 (65.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e401,611 (84.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eAdequate Plus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e57,852 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e33,787 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e24,065 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e510,897 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e245,093 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e265,804 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e334,370 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e171,197 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e163,173 (34.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e2 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e196,634 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e147,752 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e48,882 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eType of birth\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eVaginal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; 451,569 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e377,169 (66.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e74,400 (15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eCaesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e591,163 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e187,209 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e403,954 (84.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNewborn variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eBirth weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e1,002,110 (96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e540,326 (95.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e461,784 (96.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e40,662 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e24,072 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e16,590 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eBoys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e531,471 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e287,761 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e243,710 (51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.968903436988544%\" valign=\"top\"\u003e\n \u003cp\u003eGirls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.49427168576105%\" valign=\"top\"\u003e\n \u003cp\u003e511,234 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.458265139116204%\" valign=\"top\"\u003e\n \u003cp\u003e276,583 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.130932896890343%\" valign=\"top\"\u003e\n \u003cp\u003e234,651 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.947626841243862%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p-value of chi-squared test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Rates of neonatal mortality per 1000 births at term by type of hospital, Brazil.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\" valign=\"top\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.790378006872853%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePublic hospital (n=564,398)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.57044673539519%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePrivate hospital (n=478,374)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0893470790378%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.807560137457045%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512027491408935%\" valign=\"top\"\u003e\n \u003cp\u003eRate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512027491408935%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.058419243986254%\" valign=\"top\"\u003e\n \u003cp\u003eRate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0893470790378%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.807560137457045%\" valign=\"top\"\u003e\n \u003cp\u003ep-value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\" valign=\"top\"\u003e\n \u003cp\u003eEarly neonatal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512027491408935%\" valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512027491408935%\" valign=\"top\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.058419243986254%\" valign=\"top\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0893470790378%\" valign=\"top\"\u003e\n \u003cp\u003e1,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.807560137457045%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\" valign=\"top\"\u003e\n \u003cp\u003eLater neonatal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512027491408935%\" valign=\"top\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512027491408935%\" valign=\"top\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.058419243986254%\" valign=\"top\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0893470790378%\" valign=\"top\"\u003e\n \u003cp\u003e810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.807560137457045%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.742268041237114%\" valign=\"top\"\u003e\n \u003cp\u003eTotal neonatal mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\" valign=\"top\"\u003e\n \u003cp\u003e1,432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512027491408935%\" valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512027491408935%\" valign=\"top\"\u003e\n \u003cp\u003e591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.058419243986254%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.0893470790378%\" valign=\"top\"\u003e\n \u003cp\u003e2,023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.807560137457045%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p-value of Chi-square test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Early neonatal mortality at term by type of hospital.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.93948562783661%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnadjusted Hazard Ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.88048411497731%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAdjusted Hazard Ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.93948562783661%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.88048411497731%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eMother\u0026rsquo;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e0.87 (0.72,1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.45 (0.87,2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e0.90 (0.73,1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.11 (0.65,1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e20-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.55 (1.30,1.86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.22 (0.97,1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.33 (1.11,1.61)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.15 (0.89,1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eSkin color\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eNon-black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.26 (1.02,1.57)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.94 (1.30,2.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.16 (0.94,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.66 (1.10,2.50)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eMother schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eElementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e0.92 (0.79,1.07)\u003c/p\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.21 (1.06,1.38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e0.89 (0.71,1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.12 (0.98,1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.06 (0.84,1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eGestational weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.29 (2.66,4.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.11 (1.48,2.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.98 (1.58,2.49)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.09 (0.75,1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n 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width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.13 (0.93,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.65 (0.45,0.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.41 (1.07,1.85)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.36 (0.72,2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.36 (1.04,1.79)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.21 (0.64,2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n 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width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eInadequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.18 (1.00,1.38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.11 (1.55,2.87)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.19 (1.01,1.41)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.71 (1.24,2.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.59 (1.25,2.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.88 (1.17,3.04)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.56 (1.22,1.98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.46 (0.89,2.37\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eAdequate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.26 (0.96,1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.49 (0.98,2.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.23 (0.94,1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.21 (0.79,1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eAdequate Plus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.83,1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 (0.84,1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e0.91 (0.77,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.06 (0.83,1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e2 or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.03 (0.87,1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.81 (1.35,2.43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 (0.70,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.51 (1.09,2.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"41.149773071104384%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDelivery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eVaginal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eCaesarean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.71 (2.37,3.11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.22 (0.89,1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.44 (2.12,2.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.20 (0.88,1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eBirth weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.72 (5.74,7.88)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.18 (9.70,15.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.81 (4.03,5.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.05 (8.59,14.19)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eBoys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.180030257186083%\" valign=\"top\"\u003e\n \u003cp\u003eGirls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.83,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e0.84 (0.67,1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.910741301059%\" valign=\"top\"\u003e\n \u003cp\u003e0.89 (0.78,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.969742813918305%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.74 (0.60,0.92)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Late neonatal mortality at term by type of hospital.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"621\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.549839228295816%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUnadjusted Hazard Ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.942122186495176%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAdjusted Hazard Ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.549839228295816%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.942122186495176%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eFT\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eMother\u0026rsquo;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026le;19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.22 (0.99,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.15 (0.58,2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.46 (1.15,1.86)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e0.85 (0.42,1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e20-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.45 (1.15,1.82)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 (0.81,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.17 (0.92,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.07 (0.79,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eSkin color\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eNon-black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.14 (0.87,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.41 (0.80,2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.09 (0.83,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.26 (0.72,2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eMother schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eElementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e0.90 (0.75,1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e0.63 (0.38,1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.01 (0.83,1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e0.66 (0.40,1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.08 (0.80,1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.32 (0.19,0.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.16 (0.84,1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.35 (0.20,0.59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eLive with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.20 (1.02,1.41)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e0.81 (0.61,1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.18 (0.99,1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e0.94 (0.70,1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eGestational weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.25 (2.50,4.23)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.74 (1.70,4.40)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.19 (1.66,2.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.94 (1.18,3.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.00 (1.57,2.56)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.38 (0.87,2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.75 (1.36,2.25)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.29 (0.81,1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.17 (0.92,1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.10 (0.68,1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.16 (0.90,1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.09 (0.62,2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.24 (0.87,1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.33 (0.54,3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.22 (0.86,1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.02 (0.22,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eKotelchuk Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e0.48 (0.22,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003eAdequate Plus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.578778135048232%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.167202572347268%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.774919614147908%\" valign=\"top\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.508038585209004%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.971061093247588%\" valign=\"top\"\u003e\n 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\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonatal mortality, caesarean section, public hospitals","lastPublishedDoi":"10.21203/rs.3.rs-4477653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4477653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eA better understanding of neonatal mortality risk factors in Brazil would guide improvements in these indicators. Thus, this study seeks to identify risk factors associated with early and late neonatal mortality stratified by public and private hospitals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003e\u0026nbsp;This is a cohort study of newborns between January 1, 2012 and December 31, 2017. Mortality data were obtained through linkage between two Brazilian national government databases from São Paulo city. Cox regression models were used to estimate the associations between maternal and newborn characteristics on ENM (0-6 days) and LNM (7-27 days).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: In the public sector, mother's age (≥35), gestational age (\u0026lt;38 and ≥41 weeks), inadequate and intermediate Kotelchuk index, cesarean section and low birth weight (LBW) were risk factors for ENM. In the private sector, mother's skin color (black), inadequate Kotelchuk index, parity (2 or more) and LBW were risk factors for ENM, as for the mother's education (university), gestational age of 39 weeks and female sex of the newborn were protective factors for ENM. Furthermore, in the public sector, mother's age (≤19), gestational age (\u0026lt; 38 weeks), inadequate and intermediate Kotelchuk index, cesarean section and LBW were risk factors for LMN. While in the private sector gestational age of ≤ 37 weeks and LBW were risk factors for LNM, on the other hand, mother's education (university) and female sex remain protective factors for LNM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eIn Brazil, there are differences in mother's characteristics and newborn between women cared for in the public and private sectors that could influence neonatal mortality.\u003c/p\u003e","manuscriptTitle":"Early and late neonatal mortality in term newborns: Survival differences according to public and private hospitals in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-20 18:47:19","doi":"10.21203/rs.3.rs-4477653/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09b3aa59-07f3-4bf8-b398-f5ab9dc986bf","owner":[],"postedDate":"June 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-06T15:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-20 18:47:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4477653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4477653","identity":"rs-4477653","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}