Nationwide Study of Respiratory-Related Hospitalisations and Deaths in Preterm Children in Brazil: A Registry-based Study

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Although preterm birth is a major contributor to the burden of respiratory morbimortality in early childhood, most evidence comes from high-income settings. To address this gap, we examined respiratory-related hospitalisations and deaths among preterm children in Brazil. Methods : We conducted a population-based cohort study using the CIDACS Birth Cohort, including all live births in Brazil from January 1, 2011, to November 30, 2018. Preterm infants were defined as infants born before 37 weeks of gestation. We examined respiratory-related hospital admissions and deaths in children under five. Mean ratios (MR) and 95% confidence intervals (CI) were estimated using the Ghosh-Lin model; hazard ratios (HR) were estimated using Cox models. Maternal characteristics were adjusted through inverse probability weighting, with treatment probabilities estimated via entropy balancing. Results : The study included 3,239,563 live births, with 288,466 (8.9%) classified as preterm. The MR for under-five respiratory hospitalisation, comparing preterm to term births, was 1.40 (95%CI:1.38–1.42), peaking at 1.68 (1.63–1.72) between 28 and 90 days, declining to approximately 1.18 (1.10-1.28) at the fourth year. For respiratory disease deaths, the under-five HR was 3.94 (3.62–4.30). Respiratory-related mortality was highest between 28-90 days of age, with an HR of 4.66 (4.00–5.43), decreasing to 1.25 (0.62–2.51) by three years of age. Conclusion: Preterm newborns have a higher risk of respiratory illness than full-term children, particularly in their first year. This understanding can guide health strategies to address premature birth issues by identifying important periods of vulnerability. Figures Figure 1 Figure 2 Introduction Preterm birth, defined as delivery before 37 weeks of gestation, is associated with a range of short- and long-term adverse outcomes, including physical and neurodevelopmental impairments, as well as an increased risk of mortality. 1–3 Globally, the prevalence of preterm birth varies from 4% to 16%, 4 while in Brazil, it is 11%. 5 In 2019, complications related to preterm birth accounted for approximately 900,000 deaths, 4,6 making prematurity the leading cause of death among children under 5 years old. 7 Preterm infants face underdeveloped lungs and immature immune systems, exacerbating their vulnerability to lung-related disorders, respiratory infections, and hospital admissions due to respiratory complications. 8,9 While this risk decreases with increasing gestational age, 9 even late preterm infants (34–36 weeks) are significantly more likely to develop respiratory distress syndrome and other respiratory conditions compared to term infants. 10,11 Despite the highest burden of preterm births occurring in low- and middle-income countries (LMICs), 12 most studies investigating these risks have been conducted in high-income countries such as Canada 11 , the United States 2 , and the United Kingdom. 9 Some small studies conducted in LMICs have shown an increased risk of lower respiratory tract infections and asthma in preterm infants. 13–15 However, the broader adverse respiratory effects of preterm birth in these settings remain unexplored. This results in a significant gap in data from LMICs, where the burden of preterm birth is often higher due to limited healthcare resources and high rates of infectious diseases. 10,12 Furthermore, prior research often suffers from methodological limitations, such as focusing solely on the first hospitalisation and failing to account for multiple hospitalisations during the study period. 2,11,16,17 Additionally, many studies do not adjust for the higher mortality rates in preterm children compared to term children, leading to an underestimation of the true burden of respiratory complications in this population. 16–18 The present study aims to provide information about the burden of respiratory morbidity associated with preterm birth in children under five years of age in an LMIC. We used linked data from the poorest half of Brazilians applying for social programmes in the country 19 to evaluate the incidence of respiratory-related hospitalisations and deaths among children under five years of age. We also employed a robust approach that accounts for recurrent events and the increased risk of death in preterm children, ensuring a more accurate estimation of the true burden of respiratory complications in this population than in previous studies. Methods Study design We conducted a cohort study using nationwide data from live births in Brazil (CIDACS Birth Cohort) from 1st January 2011 to 30th November 2018. This cohort links information from the (i) Live Birth Information System ( Sistema de Informação de Nascidos Vivos , SINASC), which records gestational age, birth weight, and maternal demographics; (ii) the Unified Registry for Social Programmes ( Cadastro Único , CadUnico), which contains socio-economic data for families enrolled in Brazil’s social programmes; (iii) the Mortality Information System ( Sistema de Informação em Mortalidade , SIM), which records the date and cause of death; and the (iv) Hospitalisation System ( Sistema de Internação Hospitalar - Sistema Único de Saúde , SIHSUS), which notes the date and cause of hospitalisation. The cohort's baseline is the CadUnico database, reflecting the poorest segment of the Brazilian population. 19 The validation process of the linkage was done by clerical review of 2000 matches; the mean sensitivity and specificity linking SINASC and CadUnico were 95%, the values for linking SINASC and SIM were 93%, and SINASC and SIHSUS were 99%. Details about the linkage algorithm and quality of the databases were previously published. 20 Participants We excluded: (i) Births with missing data on gestational age; (ii) births with estimated gestational age using last menstrual period or missing information; (iii) Births that took place before 22 weeks or after 45 weeks of gestation or with a birthweight below 500g or higher than 6000g; (iv) Women who delivered multiple live births; (v) Live births without a documented place of residence; (vi) Data inconsistencies, such as conception dates of consecutive births occurring less than 220 days apart or records with different birthdates of the children across databases; (vii) women aged less than 10 or higher than 49 years. The exclusion of live births with estimated gestational age using the last menstrual period was intended to prevent the risk of misclassifying term births as preterm, as previous work has shown a skewed distribution in birth weights among preterm babies based on gestational age from LMP. 21 We provide details on the distribution of size for gestational age by method in the supplementary table 1. Exposure and covariates The main exposure was preterm delivery, categorised as preterm (<37 weeks) and term (≥37 weeks). The following categories for preterm were analysed in subgroup analysis: <28 weeks (extremely preterm), 28–31 weeks (very preterm), 32–36 weeks (moderate to late preterm). The preterm status was measured using completed gestational week at birth as recorded in the SINASC dataset. The covariates are maternal age at birth, marital status, parity, race/ethnicity, year of pregnancy, previous fetal loss, adequacy of number of prenatal appointments, years of schooling, state of residency, and municipality deprivation level (Brazilian deprivation index). The adequacy of the number of prenatal appointments was calculated according to the recommendations of the Brazilian Ministry of Health, which recommends a minimum of six appointments during pregnancy, with at least one in the first trimester, two in the second trimester and three in the third, trimester with monthly intervals until the 28 th week, biweekly until the 36 th week and weekly until birth. 22 Using these recommendations, we defined adequate appointments as at least two until 20 weeks, three until 27 weeks, four until 30 weeks, five until 33 weeks, and six until delivery. Outcome Our primary outcome was the number of respiratory-related hospitalisations in children under five, classified according to the International Classification of Diseases-10 (ICD-10, Chapter X, code range J00–J99). To evaluate the cause of hospitalisation, we used three-digit ICD-10 codes. If multiple hospitalisations occurred with overlapping periods and were recorded under different four-digit ICD-10 codes, they were consolidated into a single hospitalisation event, with the date of the first admission recorded as the event date. The secondary outcomes were: 1) hospitalisation for specific blocks in the Chapter X: J00-J06 (Acute upper respiratory infections), J09-J18 (Influenza and Pneumonia), J20-J22 (Other acute lower respiratory infections); 2) deaths due to respiratory diseases (ICD-10 Chapter X); 3) all-cause mortality, the all-cause mortality outcome was included as previously recommended when evaluating recurrent events. 23 Statistical Analysis We used entropy balancing, a type of inverse probability weighting, to estimate the average treatment effect size in the treated group, which captures the average differences in the outcomes in the preterm group. Entropy balancing ensures an exact balance of the covariate means while improving precision over traditional inverse probability weights. 24 We included maternal age at birth (linear and quadratic terms), marital status, parity, race/ethnicity, year of pregnancy, previous fetal loss, adequacy of number of prenatal appointments, years of schooling, state of residency, and municipality deprivation level (Brazilian deprivation index) in the model to estimate the entropy balancing weights. To account for the terminal event of death when evaluating recurrent hospitalisations, Ghosh-Lin models were used to calculate Mean Ratios (MR) and 95% confidence intervals (CI). The Ghosh-Lin method is a semi‐parametric method that estimates the marginal mean of the cumulative number of recurrent events over time, acknowledging that death is a terminal event after which no further recurrent hospitalisations can be experienced. 25 We estimated the nonparametric mean cumulative function with variance calculated using the Lawless and Nadeau estimator. We also estimated the Hazard Ratios (HR) and 95% CI using the cause-specific Cox model for death due to respiratory causes, as defined through ICD-10 codes; death from other causes was censored. Lastly, we estimated HR and 95% CI for all-cause mortality using the Cox model, as previously recommended when evaluating recurrent events. 26 We weighted all models using the inverse probability treatment weights for preterm to control for confounding. Missing data (<5%) in the covariates was addressed in the weighting estimation by missing indicators. 27 We also conducted age-stratified analyses by dividing the datasets into subgroups based on age in days: 0–27 (neonatal period), 28–90, 91–365, 366–720 (1 year), 721–1095 (2 years), 1096–1460 (3 years), and 1461–1825 (4 years). These analyses included only live births at risk within each respective period. The analysis was conducted in R 4.3.1, utilising the WeightIt, survival, and mets packages. Results [Figure 1] The study included 3,239,563 live births from 2011 to 2018; among these 288,466 (8.9%) were born preterm, and 243,453 (84.4%) were classified as moderate to late preterm (Figure 1). The mothers of preterm and term live births were similar in terms of age, race/ethnicity, and geographic distribution, but had a lower proportion of adequate number of prenatal appointments (174,536-61.7% versus 2,231,188-76.4%)(Table 1). All variables achieved satisfactory balance after weighting (Supplementary Figures 1 to 4). [Table 1] Respiratory-Related Hospitalisations [Figure 2] In the first four years of life, the preterm group experienced an average of 184 hospitalisations per 1000 children, compared with 126 hospitalisations per 1000 in the term group (Figure 2). The estimated increase was 40% more respiratory-related hospitalisations (MR: 1.40, 95% CI: 1.38 to 1.42) comparing preterm to term children during the same period. The analysis by subgroup-specific ICD-10 blocks shows similar values for influenza and pneumonia (MR: 1.35; 1.32 to 1.37), for other acute lower respiratory infections (MR: 1.40; 1.37 to 1.44), and lower values for acute upper respiratory infections (MR: 1.26; 1.19 to 1.33) (Supplementary Table 2) In the analysis by age, the highest increase in the number of hospitalisations comparing preterm and term children occurred during the period of 28-90 days, with an MR of 1.67 (95% CI: 1.63 to 1.72), and remained elevated between 91 and 365 days (MR: 1.65, 1.62 to 1.69). The risk gradually declined over time, reaching an MR of 1.18 (1.10 to 1.28) by the age of four years (Table 2). When stratified by gestational age, the burden of respiratory-related hospitalisations increased with the degree of prematurity. Compared with term children, moderate to late preterm children had 32% more hospitalisations (MR: 1.32, 95% CI: 1.30 to 1.34), whereas very preterm and extremely preterm children had 97 (MR: 1.97, 1.89 to 2.05) and 60 (MR: 1.60, 1.49 to 1.72) more hospitalisations, respectively. The highest hospitalisation rates for very preterm infants and extremely preterm infants occurred between 91 and 365 days of life, with MRs of 3.02 (2.88 to 3.17) and 4.58 (4.24 to 4.96), respectively (Figure 2 and Supplementary Table 1). At the age of 5 years, on average, 430 respiratory hospitalisations occurred per 1,000 extremely preterm children. (Figure 2) The interpretation of hospitalisation rates must consider the high all-cause mortality among preterm infants, particularly the extremely preterm group. Extremely preterm infants faced the highest mortality rate, with a neonatal mortality rate 146 times higher than that of term infants (HR: 146.62, 95% CI: 142.41 to 150.95) (Table 2). This high mortality risk likely led to a reduction in the observed hospitalisation rates for extremely preterm infants, as many infants died before they could be hospitalised. For example, the MR for respiratory-related hospitalisations in extremely preterm infants during the neonatal period was 1.37 (1.09 to 1.71), which is lower than the MR for very preterm infants (MR: 1.48, 1.27 to 1.73). (Table 2) Respiratory-Related Mortality Preterm children faced a higher rate of respiratory-related mortality compared to term children. The overall HR for respiratory-related mortality in preterm children under five was 3.95 (95% CI: 3.62–4.30). The risk was highest between 28 and 90 days of life, with an HR of 4.66 (95% CI: 4.00–5.43). The rates of respiratory-related mortality increased with the degree of prematurity, showing a clear dose-response relationship over the first four years of life. Compared with term children, moderate to late preterm children had an HR of 2.74 (95% CI: 2.48–3.03) for respiratory-related mortality. For very preterm infants, the HR was 10.76 (95% CI: 9.29 –12.46), and for extremely preterm infants, the HR reached 21.31 (95% CI: 17.42–26.06). (Table 2) The timing of the highest mortality risk varied by gestational age. For moderate to late and extremely preterm children, the highest rates occurred between 28 and 90 days (HRs: 2.94; 95% CI 2.50 to 3.45 and 25.83; 95% CI 18.28 to 36.50, respectively). However, for very preterm children, it was between 91 and 365 days (HR: 14.84; 12.06 to 18.26). (Table 2) Discussion In this nationwide cohort study, we found that children born preterm face an increased risk of respiratory-related hospitalisation and mortality compared with their term-born counterparts. The risk was particularly pronounced during the first year of life and exhibited a clear dose-response relationship, with the highest risks observed among children born at earlier gestational ages. Specifically, children born before 28 weeks of gestation had the highest mortality rates, with neonatal respiratory-related mortality exceeding 21 times that of term-born children. Similarly, the number of respiratory-related hospitalisations increased with decreasing gestational age, with extremely preterm infants experiencing nearly three times more hospitalisations between 91 and 365 days of life compared to term infants. These findings align with a growing body of evidence highlighting the long-term respiratory morbidity associated with preterm birth. 28 , 29 Preterm infants often experience disrupted alveolar development, small airway disease, and gas trapping, which can lead to structural lung abnormalities and impaired lung function throughout their lives. 28 , 30 While much of the existing research has focused on bronchopulmonary dysplasia (BPD) in extremely preterm infants, it is increasingly clear that even late preterm infants and those without BPD remain at risk for significant respiratory disease later in life. 28 , 30 , 31 This includes conditions such as transient tachypnea of the newborn, respiratory distress syndrome, pneumonia, and pulmonary hypertension, all of which occur at higher rates in preterm infants compared to term infants. 16 , 28 , 32 Additionally, preterm infants, particularly those born very premature or with BPD, are more vulnerable to severe lower respiratory tract infections, which often require frequent hospitalisations and intensive care. 10 , 33 – 36 Moreover, our study observed increased vulnerability across distinct respiratory subgroups, including acute upper respiratory infections, influenza and pneumonia, and other acute lower respiratory infections, indicating increased vulnerability in preterm children to different conditions. A key strength of this study is its large sample size, which enhances the precision and generalisability of our findings. By employing a robust methodological approach that accounts for recurrent events and competing risks, we were able to provide a more accurate estimation of the burden of respiratory complications in preterm children. This is particularly important in the context of extremely preterm births, where high mortality rates can act as a competing event, preventing the occurrence of hospitalisations, potentially masking the true burden. We also estimate the effect of preterm birth on overall mortality, providing a more comprehensive and transparent assessment of the morbidity associated with preterm birth. Our findings are consistent with those of previous studies that reported increased risks of respiratory-related hospitalisations and mortality in preterm children, although they evaluated only the first hospitalisation due to respiratory diseases. For example, studies have shown that late preterm infants have a 1.3 to 2.0 times higher risk of respiratory-related hospitalisations compared to term infants, depending on gestational age. 16 , 17 , 29 Similarly, our mortality rates for both all-cause and respiratory-related deaths are comparable to those reported in international studies, further validating our results. 2 , 37 However, our study is not without limitations. Firstly, there is potential for residual confounding due to the unavailability of data on specific variables, such as the quality and access to healthcare. Secondly, our study is susceptible to linkage errors; nonetheless, we expected that these errors would occur non-differentially, which could lead to an underestimation of the association. Thirdly, the CIDACS Birth Cohort encompasses only the most socially vulnerable live births in Brazil, which may restrict the generalisability of our findings to less vulnerable populations. Fourthly, we excluded many live births due to missing data regarding gestational age at birth in weeks. This missing data is likely attributable to the transition in 2011 when the SINASC system began recording gestational age by week instead of intervals, rather than suggesting systematic bias or confounding. Fifthly, despite the substantial sample size, specific risk periods in our stratified analyses had a low number of events, precluding precise estimates. Finally, we restricted our analysis to respiratory diseases classified under Chapter X of the ICD-10, which may have excluded pertinent respiratory conditions categorised elsewhere, such as those originating in the perinatal period (Chapter XVI). Despite these limitations, our study provides critical insights into the burden of respiratory morbidity and mortality in preterm children in an LMIC setting. These findings underscore the importance of recognising preterm birth as a key risk factor for both morbidity and mortality, which can inform the development of targeted health strategies. Interventions to address complications from preterm birth and preventive measures like immunisation against respiratory infections, including respiratory syncytial virus, are essential for improving respiratory health outcomes in preterm infants. In conclusion, preterm newborns face a significantly higher risk of respiratory illnesses compared to full-term children, particularly during their first year of life. This insight highlights critical periods of vulnerability, which can inform the development of targeted health strategies to address the challenges associated with premature birth. Recognising preterm birth as a key risk factor for both respiratory mortality and morbidity is essential for guiding preventive approaches and improving health outcomes for preterm infants, especially in resource-limited settings. Declarations Ethics: The Federal University of Bahia Institute of Health's Research Ethics Committee approved this study (CAAE registration number 73178223.1.0000.5030). Informed consent was waived because the data were deidentified and analysed under strict security procedures, in accordance with the General Data Protection Law (13,709/2018), Article 7, Item IV. Conflict of Interest Disclosures: The authors declare no conflict of interest Consent for publication: Not applicable. Role of Funder/Sponsor: The funder had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. Funding: EP acknowledges funding from the Wellcome Trust (225925/Z/22/Z). TC-S acknowledges funding from the Royal Society (NIF\R1\231435). Author Contribution TC-S conceptualised and designed the study, performed the statistical analysis, and drafted the initial manuscript. PTVF drafted the initial manuscript and critically reviewed the manuscript. EP conceptualised and designed the study, drafted the initial manuscript, and edited the manuscript. MLB guaranteed data access and critically reviewed and revised the manuscript. ASR and RCRS critically reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. Acknowledgements: Not applicable. Data Availability The relevant data are available in the manuscript and the Supplementary Information. Raw data are available upon reasonable request to the Centro de Integração de Dados e Conhecimentos para a Saúde (CIDACS). Any person who wishes to receive authorisation must: (1) be affiliated to CIDACS or be accepted as collaborators; (2) present a detailed research project together with approval by an appropriate Brazilian institutional research ethics committee; (3) provide a clear data plan restricted to the objectives of the proposed study and a summary of the analyses plan intended to guide the linkage and data extraction of the relevant set of records and variables; (4) sign terms of responsibility regarding the access and use of data; and (5) perform the analyses of datasets provided using the CIDACS data environment, a safe and secure infrastructure that provides remote access to de-identified or anonymised datasets and analysis tools. For more information: https://cidacs.bahia.fiocruz.br/ References Behrman RE, Butler AS, Outcomes I of M (US) C on UPB and AH. Mortality and Acute Complications in Preterm Infants. In: Preterm Birth: Causes, Consequences, and Prevention . 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Respiratory Outcome after Preterm Birth: A Long and Difficult Journey. Amer J Perinatol . 2016;33(11):1040-1042. doi:10.1055/s-0036-1586172 Khashu M, Narayanan M, Bhargava S, Osiovich H. Perinatal Outcomes Associated With Preterm Birth at 33 to 36 Weeks’ Gestation: A Population-Based Cohort Study. Pediatrics . 2009;123(1):109-113. doi:10.1542/peds.2007-3743 Law BJ, Langley JM, Allen U, et al. The Pediatric Investigators Collaborative Network on Infections in Canada study of predictors of hospitalization for respiratory syncytial virus infection for infants born at 33 through 35 completed weeks of gestation. Pediatr Infect Dis J . 2004;23(9):806-814. doi:10.1097/01.inf.0000137568.71589.bd Horn SD, Smout RJ. Effect of prematurity on respiratory syncytial virus hospital resource use and outcomes. J Pediatr . 2003;143(5 Suppl):S133-141. doi:10.1067/s0022-3476(03)00509-2 Willson DF, Landrigan CP, Horn SD, Smout RJ. Complications in infants hospitalized for bronchiolitis or respiratory syncytial virus pneumonia. J Pediatr . 2003;143(5 Suppl):S142-149. doi:10.1067/s0022-3476(03)00514-6 Kenmoe S, Kengne-Nde C, Modiyinji AF, Rosa GL, Njouom R. Comparison of health care resource utilization among preterm and term infants hospitalized with Human Respiratory Syncytial Virus infections: A systematic review and meta-analysis of retrospective cohort studies. PLOS ONE . 2020;15(2):e0229357. doi:10.1371/journal.pone.0229357 Ahmed AM, Grandi SM, Pullenayegum E, et al. Short-Term and Long-Term Mortality Risk After Preterm Birth. JAMA Network Open . 2024;7(11):e2445871. doi:10.1001/jamanetworkopen.2024.45871 Tables Table 1: Baseline characteristics of singleton live births Characteristic Term N = 2,951,097 Preterm N = 288,466 Overall N = 3,239,563 Age mother - group 10-17 323,620 (11.0) 40,021 (13.9) 363,641 (11.2) 18-24 1,209,107 (41.0) 109,157 (37.8) 1,318,264 (40.7) 25-29 680,729 (23.1) 59,144 (20.5) 739,873 (22.8) 30-34 448,221 (15.2) 44,348 (15.4) 492,569 (15.2) 35-49 289,420 (9.8) 35,796 (12.4) 325,216 (10.0) Age mother – years, median (IQR) 24 (20, 29) 24 (19, 30) 24 (20, 30) Years of schooling None 15,944 (0.5) 1,776 (0.6) 17,720 (0.5) 1 to 3 85,533 (2.9) 9,378 (3.3) 94,911 (2.9) 4 to 7 678,241 (23.0) 70,407 (24.4) 748,648 (23.1) 8 to 11 1,963,752 (66.5) 185,960 (64.5) 2,149,712 (66.4) ≥12 190,005 (6.4) 18,847 (6.5) 208,852 (6.4) Missing data 17,622 (0.6) 2,098 (0.7) 19,720 (0.6) Race/ ethnicity White 1,013,408 (34.3) 101,184 (35.1) 1,114,592 (34.4) Black 244,639 (8.3) 24,266 (8.4) 268,905 (8.3) Indigenous 15,278 (0.5) 1,346 (0.5) 16,624 (0.5) Mixed 1,624,497 (55.0) 155,856 (54.0) 1,780,353 (55.0) Asian 9,014 (0.3) 901 (0.3) 9,915 (0.3) Missing data 44,261 (1.5) 4,913 (1.7) 49,174 (1.5) Marital Status Single 32,610 (1.1) 3,530 (1.2) 36,140 (1.1) Married 579,969 (19.7) 54,789 (19.0) 634,758 (19.6) Divorced 1,654,045 (56.0) 164,197 (56.9) 1,818,242 (56.1) Stable union 664,496 (22.5) 63,822 (22.1) 728,318 (22.5) Widowed 4,795 (0.2) 488 (0.2) 5,283 (0.2) Missing data 15,182 (0.5) 1,640 (0.6) 16,822 (0.5) Geographic Region North 201,785 (6.8) 18,231 (6.3) 220,016 (6.8) Northeast 545,173 (18.5) 60,329 (20.9) 605,502 (18.7) Southeast 1,429,189 (48.4) 138,097 (47.9) 1,567,286 (48.4) South 579,067 (19.6) 56,127 (19.5) 635,194 (19.6) Central-west 195,883 (6.6) 15,682 (5.4) 211,565 (6.5) Deprivation Index-City 1 (lowest deprivation) 623,404 (21.1) 61,203 (21.2) 684,607 (21.1) 2 663,452 (22.5) 64,127 (22.2) 727,579 (22.5) 3 787,349 (26.7) 73,686 (25.5) 861,035 (26.6) 4 556,922 (18.9) 53,770 (18.6) 610,692 (18.9) 5 (highest deprivation) 319,970 (10.8) 35,680 (12.4) 355,650 (11.0) Number of prenatal appointments None 65,145 (2.2) 15,044 (5.2) 80,189 (2.5) 1 to 3 228,960 (7.8) 50,061 (17.4) 279,021 (8.6) 4 to 6 771,292 (26.1) 110,783 (38.4) 882,075 (27.2) ≥7 1,858,575 (63.0) 107,185 (37.2) 1,965,760 (60.7) Missing data 27,125 (0.9) 5,393 (1.9) 32,518 (1.0) Adequate number of prenatal appointments 2,231,188 (76.4) 174,536 (61.7) 2,405,724 (75.1) Number of previous pregnancies 0 1,866,754 (63.3) 174,366 (60.4) 2,041,120 (64.4) ≥1 1,022,395 (34.6) 108,326 (37.6) 1,130,721 (35.6) Missing data 61,948 (2.1) 5,774 (2.0) 67,722 (2.1) Previous fetal loss 509,346 (17.3) 61,009 (21.1) 570,355 (18.4) Missing data 123,505 (4.2) 10,403 (3.6) 133,908 (4.1) Gestational age, median (IQR) 39.00 (38.00 - 40.00) 35.00 (33.00 - 36.00) 39.00 (38.00 - 40.00) Gestational age method Physical Exam 1,598,288 (54.2) 155,498 (53.9) 1,753,786 (54.1) Ultrasonography 1,352,809 (45.8) 132,968 (46.1) 1,485,777 (45.9) Preterm category moderate to late preterm (32 to 37 weeks) 243,451 (84.4) 243,451 (84.4) very preterm (28 to less than 32 weeks) 30,246 (10.5) 30,246 (10.5) extremely preterm (less than 28 weeks) 14,769 (5.1) 14,769 (5.1) Sex of live birth Male 1,509,774 (51.2) 152,266 (52.8) 1,662,040 (51.3) Female 1,441,323 (48.8) 136,200 (47.2) 1,577,523 (48.7) Year of birth 2011 127,493 (4.3) 12,766 (4.4) 140,259 (4.3) 2012 284,001 (9.6) 28,911 (10.0) 312,912 (9.7) 2013 383,701 (13.0) 37,331 (12.9) 421,032 (13.0) 2014 474,401 (16.1) 45,161 (15.7) 519,562 (16.0) 2015 490,522 (16.6) 46,295 (16.0) 536,817 (16.6) 2016 402,268 (13.6) 39,175 (13.6) 441,443 (13.6) 2017 416,982 (14.1) 41,562 (14.4) 458,544 (14.2) 2018 371,729 (12.6) 37,265 (12.9) 408,994 (12.6) Birth weight (g), median (IQR) 3,250 (2,970, 3,550) 2,350 (1,860, 2,722) 3,205 (2,890, 3,520) Low birth weight (<2500g) 112,679 (3.8) 173,389 (60.1) 286,068 (8.8) Weight for gestational age SGA 216,159 (7.3) 28,214 (9.8) 244,373 (7.5) AGA 2,351,054 (79.7) 222,572 (77.2) 2,573,626 (79.4) LGA 383,884 (13.0) 37,680 (13.1) 421,564 (13.0) Congenital Anomaly 25,078 (0.8) 7,251 (2.5) 32,329 (1.0) Missing data 17,797 (0.6) 2,520 (0.9) 20,317 (0.6) Delayed antenatal care 697,108 (23.6) 70,600 (24.5) 767,708 (23.7) Missing data 190,268 (6.4) 32,064 (11.1) 222,332 (6.9) Apgar 5', median (IQR) 9.00 (9.00, 10.00) 9.00 (8.00, 10.00) 9.00 (9.00, 10.00) Missing data 33,940 (1.2) 4,674 (1.6) 38,614 (1.2) Low Apgar <7 21,537 (0.7) 16,034 (5.6) 37,571 (1.2) Missing data 33,940 (1.2) 4,674 (1.6) 38,614 (1.2) IQR: Interquartile range; Weight for gestational age was defined based on intergrowth charts and comprised: (1) small for gestational age (SGA) – i.e. birth weight <10th percentile for sex and gestational age; (2) Appropriate for gestational age (AGA) – i.e. birthweight between 10th and 90th percentiles for sex and gestational age; (3) Large for gestational age (LGA) – i.e. birthweight > 90th percentile for sex and gestational age. Table 2: Mean ratios for the number of respiratory-related hospitalisations and hazard ratios for respiratory-related mortality and all-cause mortality comparing preterm and term children. Category No. children Person years No respiratory hospitalisations Mean Ratio (95% CI) No. respiratory deaths Hazard Ratio - Respiratory Death (95% CI) No. deaths Hazard Ratio - All cause mortality (95% CI) Term 2951097 9441592 292856 Ref 2024 Ref 18357 Ref Preterm 288466 854531 40072 1.40 (1.38 to 1.42) 783 3.95 (3.62 to 4.30) 22485 11.99 (11.74 to 12.24) Moderate to late 243453 753574 31814 1.32 (1.30 to 1.34) 479 2.74 (2.48 to 3.03) 8001 4.92 (4.79 to 5.06) Very 30246 80174 5892 1.97 (1.89 to 2.05) 202 10.76 (9.29 to 12.46) 5904 31.93 (30.95 to 32.94) Extremely 14767 20783 2366 1.60 (1.49 to 1.72) 102 21.31 (17.42 to 26.06) 8580 146.62 (142.41 to 150.95) 0-27 Term 2951097 216671 11504 Ref 87 Ref 7079 Ref Preterm 288466 20196 1602 1.41 (1.34 to 1.49) 34 3.69 (2.46 to 5.55) 17000 22.95 (22.28 to 23.65) Moderate to late 243453 17617 1353 1.41 (1.33 to 1.49) 21 2.61 (1.61 to 4.24) 4917 7.75 (7.47 to 8.06) Very 30246 1957 171 1.48 (1.27 to 1.73) <5 N/A 4540 59.21 (56.90 to 61.61) Extremely 14767 622 78 1.37 (1.09 to 1.71) 9 N/A 7543 267.53 (258.05 to 277.36) 28-90 Term 2915024 497049 44869 Ref 559 Ref 3308 Ref Preterm 268609 45518 7056 1.68 (1.63 to 1.72) 261 4.66 (4.00 to 5.43) 2794 8.23 (7.81 to 8.68) Moderate to late 236067 40130 5885 1.58 (1.54 to 1.63) 157 3.18 (2.65 to 3.81) 1328 4.45 (4.17 to 4.75) Very 25413 4252 882 2.26 (2.11 to 2.42) 69 13.00 (10.08 to 16.77) 772 23.82 (21.97 to 25.83) Extremely 7129 1136 289 2.62 (2.32 to 2.95) 35 25.83 (18.28 to 36.50) 694 82.12 (75.42 to 89.41) 91-365 Term 2847010 2025405 113189 Ref 771 Ref 4206 Ref Preterm 259830 183921 17174 1.65 (1.62 to 1.69) 345 4.58 (4.02 to 5.23) 1905 4.52 (4.28 to 4.78) Moderate to late 229434 162521 13135 1.43 (1.40 to 1.46) 195 2.94 (2.50 to 3.45) 1176 3.17 (2.96 to 3.38) Very 24106 17012 2884 3.02 (2.88 to 3.17) 105 14.84 (12.06 to 18.26) 459 11.56 (10.48 to 12.75) Extremely 6290 4389 1155 4.58 (4.24 to 4.96) 45 25.44 (18.77 to 34.47) 270 27.20 (24.01 to 30.82) 1 year Term 2532640 2326565 66898 Ref 383 Ref 1924 Ref Preterm 228725 209739 7992 1.34 (1.30 to 1.37) 92 2.46 (1.95 to 3.10) 473 2.53 (2.28 to 2.80) Moderate to late 202207 185335 6322 1.20 (1.16 to 1.23) 66 2.01 (1.54 to 2.61) 338 2.05 (1.83 to 2.31) Very 21091 19414 1146 2.08 (1.93 to 2.24) 16 4.51 (2.73 to 7.47) 83 4.69 (3.76 to 5.85) Extremely 5427 4990 524 3.63 (3.22 to 4.09) 10 11.06 (5.88 to 20.80) 52 11.54 (8.74 to 15.22) 2 years Term 2116767 1919681 31069 Ref 129 Ref 947 Ref Preterm 190217 172584 3518 1.26 (1.21 to 1.32) 36 3.12 (2.14 to 4.55) 155 1.74 (1.46 to 2.06) Moderate to late 167962 152191 2848 1.16 (1.11 to 1.21) 28 2.75 (1.81 to 4.16) 114 1.45 (1.19 to 1.77) Very 17706 16222 474 1.81 (1.62 to 2.03) 5 N/A 31 3.66 (2.56 to 5.25) Extremely 4549 4171 196 2.87 (2.39 to 3.45) <5 N/A 10 4.56 (2.45 to 8.52) 3 years Term 1708772 1469740 16621 Ref 75 Ref 562 Ref Preterm 153254 132408 1795 1.21 (1.14 to 1.28) 9 N/A 100 1.79 (1.44 to 2.22) Moderate to late 134932 116568 1493 1.14 (1.08 to 1.22) 7 N/A 76 1.55 (1.21 to 1.97) Very 14565 12591 214 1.52 (1.28 to 1.81) <5 N/A 17 3.12 (1.92 to 5.06) Extremely 3757 3249 88 2.38 (1.83 to 3.10) <5 N/A 7 N/A 4 years N/A Term 1219541 986481 8706 Ref 20 Ref 331 Ref Preterm 110538 90165 935 1.18 (1.10 to 1.28) 6 N/A 58 1.78 (1.34 to 2.37) Moderate to late 97233 79213 778 1.12 (1.03 to 1.22) 5 N/A 52 1.81 (1.35 to 2.44) Very 10594 8727 121 1.59 (1.29 to 1.95) <5 N/A <5 N/A Extremely 2711 2225 36 1.82 (1.19 to 2.80) <5 N/A <5 N/A N/A: To ensure reliable estimates, only periods with at least 10 events in each group were estimated. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 13 Dec, 2025 Read the published version in Respiratory Research → Version 1 posted Editorial decision: Revision requested 28 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 06 Sep, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 01 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Globally, the prevalence of preterm birth varies from 4% to 16%,\u003csup\u003e4\u003c/sup\u003e while in Brazil, it is 11%.\u003csup\u003e5\u003c/sup\u003e In 2019, complications related to preterm birth accounted for approximately 900,000 deaths,\u003csup\u003e4,6\u003c/sup\u003e making prematurity the leading cause of death among children under 5 years old.\u003csup\u003e7\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePreterm infants face underdeveloped lungs and immature immune systems, exacerbating their vulnerability to lung-related disorders, respiratory infections, and hospital admissions due to respiratory complications.\u003csup\u003e8,9\u003c/sup\u003e While this risk decreases with increasing gestational age,\u003csup\u003e9\u003c/sup\u003e even late preterm infants (34\u0026ndash;36 weeks) are significantly more likely to develop respiratory distress syndrome and other respiratory conditions compared to term infants.\u003csup\u003e10,11\u003c/sup\u003e Despite the highest burden of preterm births occurring in low- and middle-income countries (LMICs),\u003csup\u003e12\u003c/sup\u003e most studies investigating these risks have been conducted in high-income countries such as Canada\u003csup\u003e11\u003c/sup\u003e , the United States\u003csup\u003e2\u003c/sup\u003e, and the United Kingdom.\u003csup\u003e9\u003c/sup\u003e Some small studies conducted in LMICs have shown an increased risk of lower respiratory tract infections and asthma in preterm infants.\u003csup\u003e13\u0026ndash;15\u003c/sup\u003e \u003cstrong\u003eHowever, the broader adverse respiratory effects of preterm birth in these settings remain unexplored.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis results in a significant gap in data from LMICs, where the burden of preterm birth is often higher due to limited healthcare resources and high rates of infectious diseases.\u003csup\u003e10,12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFurthermore, prior research often suffers from methodological limitations, such as focusing solely on the first hospitalisation and failing to account for multiple hospitalisations during the study period.\u003csup\u003e2,11,16,17\u003c/sup\u003e Additionally, many studies do not adjust for the higher mortality rates in preterm children compared to term children, leading to an underestimation of the true burden of respiratory complications in this population.\u0026nbsp;\u003csup\u003e16\u0026ndash;18\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe present study aims to provide information about the burden of respiratory morbidity associated with preterm birth in children under five years of age in an LMIC. We used linked data from\u0026nbsp;the poorest half of Brazilians applying for social programmes in the country\u003csup\u003e19\u003c/sup\u003e to evaluate the incidence of respiratory-related hospitalisations and deaths among children under five years of age. We also employed a robust approach that accounts for recurrent events and the increased risk of death in preterm children, ensuring a more accurate estimation of the true burden of respiratory complications in this population than in previous studies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a cohort study using nationwide data from live births in Brazil (CIDACS Birth Cohort) from 1st January 2011 to 30th November 2018. This cohort links information from the (i) Live Birth Information System (\u003cem\u003eSistema de Informa\u0026ccedil;\u0026atilde;o de Nascidos Vivos\u003c/em\u003e, SINASC), which records gestational age, birth weight, and maternal demographics; (ii) the Unified Registry for Social Programmes (\u003cem\u003eCadastro \u0026Uacute;nico\u003c/em\u003e, CadUnico), which contains socio-economic data for families enrolled in Brazil\u0026rsquo;s social programmes; (iii) the Mortality Information System (\u003cem\u003eSistema de Informa\u0026ccedil;\u0026atilde;o em Mortalidade\u003c/em\u003e, SIM), which records the date and cause of death; and the (iv) Hospitalisation System (\u003cem\u003eSistema de Interna\u0026ccedil;\u0026atilde;o Hospitalar - Sistema \u0026Uacute;nico de Sa\u0026uacute;de\u003c/em\u003e, SIHSUS), which notes the date and cause of hospitalisation. The cohort\u0026apos;s baseline is the CadUnico database, reflecting the poorest segment of the Brazilian population.\u003csup\u003e19\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe validation process of the linkage was done by clerical review of 2000 matches; the mean sensitivity and specificity linking SINASC and CadUnico were 95%, the values for linking SINASC and SIM were 93%, and SINASC and SIHSUS were 99%. Details about the linkage algorithm and quality of the databases were previously published.\u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe excluded: (i) Births with missing data on gestational age; (ii) births with estimated gestational age using last menstrual period or missing information; (iii) Births that took place before 22 weeks or after 45 weeks of gestation or with a birthweight below 500g or higher than 6000g; (iv) Women who delivered multiple live births; (v) Live births without a documented place of residence; (vi) Data inconsistencies, such as conception dates of consecutive births occurring less than 220 days apart or records with different birthdates of the children across databases; (vii) women aged less than 10 or higher than 49 years.\u003c/p\u003e\n\u003cp\u003eThe exclusion of live births with estimated gestational age using the last menstrual period was intended to prevent the risk of misclassifying term births as preterm, as previous work has shown a skewed distribution in birth weights among preterm babies based on gestational age from LMP.\u003csup\u003e21\u003c/sup\u003e We provide details on the distribution of size for gestational age by method in the supplementary table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure and covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main exposure was preterm delivery, categorised as preterm (\u0026lt;37 weeks) and term (\u0026ge;37 weeks). The following categories for preterm were analysed in subgroup analysis: \u0026lt;28 weeks (extremely preterm), 28\u0026ndash;31 weeks (very preterm), 32\u0026ndash;36 weeks (moderate to late preterm). The preterm status was measured using completed gestational week at birth as recorded in the SINASC dataset. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe covariates are maternal\u0026nbsp;age at birth, marital status, parity, race/ethnicity, year of pregnancy, previous fetal loss, adequacy of number of prenatal appointments, years of schooling, state of residency, and municipality deprivation level (Brazilian deprivation index). The adequacy of the number of prenatal appointments was calculated according to the recommendations of the Brazilian Ministry of Health, which recommends a minimum of six appointments during pregnancy, with at least one in the first trimester, two in the second trimester and three in the third, trimester with monthly intervals until the 28\u003csup\u003eth\u003c/sup\u003e week, biweekly until the 36\u003csup\u003eth\u003c/sup\u003e week and weekly until birth.\u003csup\u003e22\u003c/sup\u003e Using these recommendations, we defined adequate appointments as at least two until 20 weeks, three until 27 weeks, four until 30 weeks, five until 33 weeks, and six until delivery.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur primary outcome was the number of respiratory-related hospitalisations in children under five, classified according to the International Classification of Diseases-10 (ICD-10, Chapter X, code range J00\u0026ndash;J99). To evaluate the cause of hospitalisation, we used three-digit ICD-10 codes. If multiple hospitalisations occurred with overlapping periods and were recorded under different four-digit ICD-10 codes, they were consolidated into a single hospitalisation event, with the date of the first admission recorded as the event date.\u003c/p\u003e\n\u003cp\u003eThe secondary outcomes were: 1) hospitalisation for specific blocks in the Chapter X: J00-J06 (Acute upper respiratory infections), J09-J18 (Influenza and Pneumonia), J20-J22 (Other acute lower respiratory infections); 2) deaths due to respiratory diseases (ICD-10 Chapter X); 3) all-cause mortality, the all-cause mortality outcome was included as previously recommended when evaluating recurrent events.\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We used entropy balancing, a type of inverse probability weighting, to estimate the average treatment effect size in the treated group, which captures the average differences in the outcomes in the preterm group. Entropy balancing ensures an exact balance of the covariate means while improving precision over traditional inverse probability weights.\u003csup\u003e24\u003c/sup\u003e We included\u0026nbsp;maternal\u0026nbsp;age at birth (linear and quadratic terms), marital status, parity, race/ethnicity, year of pregnancy, previous fetal loss, adequacy of number of prenatal appointments, years of schooling, state of residency, and municipality deprivation level (Brazilian deprivation index) in the model to estimate the entropy balancing weights.\u003c/p\u003e\n\u003cp\u003eTo account for the terminal event of death when evaluating recurrent hospitalisations, Ghosh-Lin models were used to calculate Mean Ratios (MR) and 95% confidence intervals (CI). The Ghosh-Lin method is a semi‐parametric method that estimates the marginal mean of the cumulative number of recurrent events over time, acknowledging that death is a terminal event after which no further recurrent hospitalisations can be experienced.\u003csup\u003e25\u003c/sup\u003e We estimated the nonparametric mean cumulative function with variance calculated using the Lawless and Nadeau estimator.\u003c/p\u003e\n\u003cp\u003eWe also estimated the Hazard Ratios (HR) and 95% CI using the cause-specific Cox model for death due to respiratory causes, as defined through ICD-10 codes; death from other causes was censored. Lastly, we estimated HR and 95% CI for all-cause mortality using the Cox model, as previously recommended when evaluating recurrent events.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWe weighted all models using the inverse probability treatment weights for preterm to control for confounding. Missing data (\u0026lt;5%) in the covariates was addressed in the weighting estimation by missing indicators.\u003csup\u003e27\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWe also conducted age-stratified analyses by dividing the datasets into subgroups based on age in days: 0\u0026ndash;27 (neonatal period), 28\u0026ndash;90, 91\u0026ndash;365, 366\u0026ndash;720 (1 year), 721\u0026ndash;1095 (2 years), 1096\u0026ndash;1460 (3 years), and 1461\u0026ndash;1825 (4 years). These analyses included only live births at risk within each respective period. The analysis was conducted in R 4.3.1, utilising the WeightIt, survival, and mets packages.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e[Figure 1]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 3,239,563 live births from 2011 to 2018; among these 288,466 (8.9%) were born preterm, and 243,453 (84.4%) were classified as moderate to late preterm (Figure 1). The mothers of preterm and term live births were similar in terms of age, race/ethnicity, and geographic distribution, but had a lower proportion of adequate number of prenatal appointments (174,536-61.7% versus 2,231,188-76.4%)(Table 1). All variables achieved satisfactory balance after weighting (Supplementary Figures 1 to 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Table 1]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRespiratory-Related Hospitalisations\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[Figure 2]\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the first four years of life, the preterm group experienced an average of 184 hospitalisations per 1000 children, compared with 126 hospitalisations per 1000 in the term group (Figure 2). The estimated increase was 40% more respiratory-related hospitalisations (MR: 1.40, 95% CI: 1.38 to 1.42) comparing preterm to term children during the same period. The analysis by subgroup-specific ICD-10 blocks shows similar values for influenza and pneumonia (MR: 1.35; 1.32 to 1.37), for other acute lower respiratory infections (MR: 1.40; 1.37 to 1.44), and lower values for acute upper respiratory infections (MR: 1.26; 1.19 to 1.33) (Supplementary Table 2)\u003c/p\u003e\n\u003cp\u003eIn the analysis by age, the highest increase in the number of hospitalisations comparing preterm and term children occurred during the period of 28-90 days, with an MR of 1.67 (95% CI: 1.63 to 1.72), and remained elevated between 91 and 365 days (MR: 1.65, 1.62 to 1.69). The risk gradually declined over time, reaching an MR of 1.18 (1.10 to 1.28) by the age of four years (Table 2).\u003c/p\u003e\n\u003cp\u003eWhen stratified by gestational age, the burden of respiratory-related hospitalisations increased with the degree of prematurity. Compared with term children, moderate to late preterm children had 32% more hospitalisations (MR: 1.32, 95% CI: 1.30 to 1.34), whereas very preterm and extremely preterm children had 97 (MR: 1.97, 1.89 to 2.05) and 60 (MR: 1.60, 1.49 to 1.72) more hospitalisations, respectively. The highest hospitalisation rates for very preterm infants and extremely preterm infants occurred between 91 and 365 days of life, with MRs of 3.02 (2.88 to 3.17) and 4.58 (4.24 to 4.96), respectively (Figure 2 and Supplementary Table 1). At the age of 5 years, on average, 430 respiratory hospitalisations occurred per 1,000 extremely preterm children. (Figure 2)\u003c/p\u003e\n\u003cp\u003eThe interpretation of hospitalisation rates must consider the high all-cause mortality among preterm infants, particularly the extremely preterm group. Extremely preterm infants faced the highest mortality rate, with a neonatal mortality rate 146 times higher than that of term infants (HR: 146.62, 95% CI: 142.41 to 150.95) (Table 2). This high mortality risk likely led to a reduction in the observed hospitalisation rates for extremely preterm infants, as many infants died before they could be hospitalised. For example, the MR for respiratory-related hospitalisations in extremely preterm infants during the neonatal period was 1.37 (1.09 to 1.71), which is lower than the MR for very preterm infants (MR: 1.48, 1.27 to 1.73). (Table 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRespiratory-Related Mortality\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreterm children faced a higher rate of respiratory-related mortality compared to term children. The overall HR for respiratory-related mortality in preterm children under five was 3.95 (95% CI: 3.62\u0026ndash;4.30). The risk was highest between 28 and 90 days of life, with an HR of 4.66 (95% CI: 4.00\u0026ndash;5.43).\u003c/p\u003e\n\u003cp\u003eThe rates of respiratory-related mortality increased with the degree of prematurity, showing a clear dose-response relationship over the first four years of life. Compared with term children, moderate to late preterm children had an HR of 2.74 (95% CI: 2.48\u0026ndash;3.03) for respiratory-related mortality. For very preterm infants, the HR was 10.76 (95% CI: 9.29 \u0026ndash;12.46), and for extremely preterm infants, the HR reached 21.31 (95% CI: 17.42\u0026ndash;26.06). (Table 2)\u003c/p\u003e\n\u003cp\u003eThe timing of the highest mortality risk varied by gestational age. For moderate to late and extremely preterm children, the highest rates occurred between 28 and 90 days (HRs: 2.94; 95% CI 2.50 to 3.45 and 25.83; 95% CI 18.28 to 36.50, respectively). However, for very preterm children, it was between 91 and 365 days (HR: 14.84; 12.06 to 18.26). (Table 2)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this nationwide cohort study, we found that children born preterm face an increased risk of respiratory-related hospitalisation and mortality compared with their term-born counterparts. The risk was particularly pronounced during the first year of life and exhibited a clear dose-response relationship, with the highest risks observed among children born at earlier gestational ages. Specifically, children born before 28 weeks of gestation had the highest mortality rates, with neonatal respiratory-related mortality exceeding 21 times that of term-born children. Similarly, the number of respiratory-related hospitalisations increased with decreasing gestational age, with extremely preterm infants experiencing nearly three times more hospitalisations between 91 and 365 days of life compared to term infants.\u003c/p\u003e\u003cp\u003eThese findings align with a growing body of evidence highlighting the long-term respiratory morbidity associated with preterm birth.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Preterm infants often experience disrupted alveolar development, small airway disease, and gas trapping, which can lead to structural lung abnormalities and impaired lung function throughout their lives.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e While much of the existing research has focused on bronchopulmonary dysplasia (BPD) in extremely preterm infants, it is increasingly clear that even late preterm infants and those without BPD remain at risk for significant respiratory disease later in life.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e This includes conditions such as transient tachypnea of the newborn, respiratory distress syndrome, pneumonia, and pulmonary hypertension, all of which occur at higher rates in preterm infants compared to term infants.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Additionally, preterm infants, particularly those born very premature or with BPD, are more vulnerable to severe lower respiratory tract infections, which often require frequent hospitalisations and intensive care.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Moreover, our study observed increased vulnerability across distinct respiratory subgroups, including acute upper respiratory infections, influenza and pneumonia, and other acute lower respiratory infections, indicating increased vulnerability in preterm children to different conditions.\u003c/p\u003e\u003cp\u003eA key strength of this study is its large sample size, which enhances the precision and generalisability of our findings. By employing a robust methodological approach that accounts for recurrent events and competing risks, we were able to provide a more accurate estimation of the burden of respiratory complications in preterm children. This is particularly important in the context of extremely preterm births, where high mortality rates can act as a competing event, preventing the occurrence of hospitalisations, potentially masking the true burden. We also estimate the effect of preterm birth on overall mortality, providing a more comprehensive and transparent assessment of the morbidity associated with preterm birth.\u003c/p\u003e\u003cp\u003eOur findings are consistent with those of previous studies that reported increased risks of respiratory-related hospitalisations and mortality in preterm children, although they evaluated only the first hospitalisation due to respiratory diseases. For example, studies have shown that late preterm infants have a 1.3 to 2.0 times higher risk of respiratory-related hospitalisations compared to term infants, depending on gestational age.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Similarly, our mortality rates for both all-cause and respiratory-related deaths are comparable to those reported in international studies, further validating our results.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHowever, our study is not without limitations. Firstly, there is potential for residual confounding due to the unavailability of data on specific variables, such as the quality and access to healthcare. Secondly, our study is susceptible to linkage errors; nonetheless, we expected that these errors would occur non-differentially, which could lead to an underestimation of the association. Thirdly, the CIDACS Birth Cohort encompasses only the most socially vulnerable live births in Brazil, which may restrict the generalisability of our findings to less vulnerable populations. Fourthly, we excluded many live births due to missing data regarding gestational age at birth in weeks. This missing data is likely attributable to the transition in 2011 when the SINASC system began recording gestational age by week instead of intervals, rather than suggesting systematic bias or confounding. Fifthly, despite the substantial sample size, specific risk periods in our stratified analyses had a low number of events, precluding precise estimates. Finally, we restricted our analysis to respiratory diseases classified under Chapter X of the ICD-10, which may have excluded pertinent respiratory conditions categorised elsewhere, such as those originating in the perinatal period (Chapter XVI).\u003c/p\u003e\u003cp\u003eDespite these limitations, our study provides critical insights into the burden of respiratory morbidity and mortality in preterm children in an LMIC setting. These findings underscore the importance of recognising preterm birth as a key risk factor for both morbidity and mortality, which can inform the development of targeted health strategies. Interventions to address complications from preterm birth and preventive measures like immunisation against respiratory infections, including respiratory syncytial virus, are essential for improving respiratory health outcomes in preterm infants.\u003c/p\u003e\u003cp\u003eIn conclusion, preterm newborns face a significantly higher risk of respiratory illnesses compared to full-term children, particularly during their first year of life. This insight highlights critical periods of vulnerability, which can inform the development of targeted health strategies to address the challenges associated with premature birth. Recognising preterm birth as a key risk factor for both respiratory mortality and morbidity is essential for guiding preventive approaches and improving health outcomes for preterm infants, especially in resource-limited settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics:\u003c/h2\u003e\n\u003cp\u003eThe Federal University of Bahia Institute of Health\u0026apos;s Research Ethics Committee approved this study (CAAE registration number 73178223.1.0000.5030). Informed consent was waived because the data were deidentified and analysed under strict security procedures, in accordance with the General Data Protection Law (13,709/2018), Article 7, Item IV.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest Disclosures:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003ch2\u003eConsent for publication:\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eRole of Funder/Sponsor:\u003c/h2\u003e\n\u003cp\u003eThe funder had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eEP acknowledges funding from the Wellcome Trust (225925/Z/22/Z). TC-S acknowledges funding from the Royal Society (NIF\\R1\\231435).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eTC-S conceptualised and designed the study, performed the statistical analysis, and drafted the initial manuscript. PTVF drafted the initial manuscript and critically reviewed the manuscript. EP conceptualised and designed the study, drafted the initial manuscript, and edited the manuscript. MLB guaranteed data access and critically reviewed and revised the manuscript. ASR and RCRS critically reviewed and revised the manuscript. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe relevant data are available in the manuscript and the Supplementary Information. Raw data are available upon reasonable request to the Centro de Integra\u0026ccedil;\u0026atilde;o de Dados e Conhecimentos para a Sa\u0026uacute;de (CIDACS). Any person who wishes to receive authorisation must: (1) be affiliated to CIDACS or be accepted as collaborators; (2) present a detailed research project together with approval by an appropriate Brazilian institutional research ethics committee; (3) provide a clear data plan restricted to the objectives of the proposed study and a summary of the analyses plan intended to guide the linkage and data extraction of the relevant set of records and variables; (4) sign terms of responsibility regarding the access and use of data; and (5) perform the analyses of datasets provided using the CIDACS data environment, a safe and secure infrastructure that provides remote access to de-identified or anonymised datasets and analysis tools. For more information: https://cidacs.bahia.fiocruz.br/\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBehrman RE, Butler AS, Outcomes I of M (US) C on UPB and AH. Mortality and Acute Complications in Preterm Infants. In: \u003cem\u003ePreterm Birth: Causes, Consequences, and Prevention\u003c/em\u003e. National Academies Press (US); 2007. Accessed January 12, 2025. https://www.ncbi.nlm.nih.gov/books/NBK11385/\u003c/li\u003e\n\u003cli\u003eBell EF, Hintz SR, Hansen NI, et al. Mortality, In-Hospital Morbidity, Care Practices, and 2-Year Outcomes for Extremely Preterm Infants in the US, 2013-2018. \u003cem\u003eJAMA\u003c/em\u003e. 2022;327(3):248-263. doi:10.1001/jama.2021.23580\u003c/li\u003e\n\u003cli\u003eRespiratory Morbidity in Late Preterm Births. \u003cem\u003eJAMA\u003c/em\u003e. 2010;304(4):419-425. doi:10.1001/jama.2010.1015\u003c/li\u003e\n\u003cli\u003eWHO. Preterm birth. January 15, 2025. 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Respiratory consequences of late preterm birth. \u003cem\u003ePaediatric Respiratory Reviews\u003c/em\u003e. 2015;16(3):182-188. doi:10.1016/j.prrv.2014.12.001\u003c/li\u003e\n\u003cli\u003eCrockett LK, Brownell MD, Heaman MI, Ruth CA, Prior HJ. Examining Early Childhood Health Outcomes of Children Born Late Preterm in Urban Manitoba. \u003cem\u003eMatern Child Health J\u003c/em\u003e. 2017;21(12):2141-2148. doi:10.1007/s10995-017-2329-5\u003c/li\u003e\n\u003cli\u003eLiang X, Lyu Y, Li J, Li Y, Chi C. Global, regional, and national burden of preterm birth, 1990\u0026ndash;2021: a systematic analysis from the global burden of disease study 2021. \u003cem\u003eeClinicalMedicine\u003c/em\u003e. 2024;76:102840. doi:10.1016/j.eclinm.2024.102840\u003c/li\u003e\n\u003cli\u003eBeen JV, Lugtenberg MJ, Smets E, et al. Preterm Birth and Childhood Wheezing Disorders: A Systematic Review and Meta-Analysis. Lanphear BP, ed. \u003cem\u003ePLoS Med\u003c/em\u003e. 2014;11(1):e1001596. doi:10.1371/journal.pmed.1001596\u003c/li\u003e\n\u003cli\u003eChaya S, Simpson SJ, Marozva N, et al. The effect of moderate to late preterm birth on lung function over the first 5 years of life in a South African birth cohort. \u003cem\u003eERJ Open Research\u003c/em\u003e. Published online January 8, 2025. doi:10.1183/23120541.00733-2024\u003c/li\u003e\n\u003cli\u003eDiggikar S, Paul A, Razak A, Chandrasekaran M, Swamy RS. Respiratory infections in children born preterm in low and middle-income countries: A systematic review. \u003cem\u003ePediatric Pulmonology\u003c/em\u003e. 2022;57(12):2903-2914. doi:10.1002/ppul.26128\u003c/li\u003e\n\u003cli\u003eMcIntire DD, Leveno KJ. Neonatal mortality and morbidity rates in late preterm births compared with births at term. \u003cem\u003eObstet Gynecol\u003c/em\u003e. 2008;111(1):35-41. doi:10.1097/01.AOG.0000297311.33046.73\u003c/li\u003e\n\u003cli\u003eGutvirtz G, Wainstock T, Sheiner E, Pariente G. Prematurity and Long-Term Respiratory Morbidity\u0026mdash;What Is the Critical Gestational Age Threshold? \u003cem\u003eJournal of Clinical Medicine\u003c/em\u003e. 2022;11(3):751. doi:10.3390/jcm11030751\u003c/li\u003e\n\u003cli\u003eCharles-Nelson A, Katsahian S, Schramm C. How to analyze and interpret recurrent events data in the presence of a terminal event: An application on readmission after colorectal cancer surgery. \u003cem\u003eStatistics in Medicine\u003c/em\u003e. 2019;38(18):3476-3502. doi:10.1002/sim.8168\u003c/li\u003e\n\u003cli\u003eBarreto ML, Ichihara MY, Pescarini JM, et al. Cohort Profile: The 100 Million Brazilian Cohort. \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e. 2022;51(2):e27-e38. doi:10.1093/ije/dyab213\u003c/li\u003e\n\u003cli\u003eAlmeida D, Gorender D, Ichihara MY, et al. Examining the quality of record linkage process using nationwide Brazilian administrative databases to build a large birth cohort. \u003cem\u003eBMC Medical Informatics and Decision Making\u003c/em\u003e. 2020;20(1):173. doi:10.1186/s12911-020-01192-0\u003c/li\u003e\n\u003cli\u003eHenriques LB, Alves EB, Vieira FM dos SB, et al. Acur\u0026aacute;cia da determina\u0026ccedil;\u0026atilde;o da idade gestacional no Sistema de Informa\u0026ccedil;\u0026otilde;es sobre Nascidos Vivos (SINASC): um estudo de base populacional. \u003cem\u003eCad Sa\u0026uacute;de P\u0026uacute;blica\u003c/em\u003e. 2019;35:e00098918. doi:https://doi.org/10.1590/0102-311X00098918\u003c/li\u003e\n\u003cli\u003eLeal M do C, Esteves-Pereira AP, Viellas EF, Domingues RMSM, Gama SGN da. Assist\u0026ecirc;ncia pr\u0026eacute;-natal na rede p\u0026uacute;blica do Brasil. \u003cem\u003eRev Sa\u0026uacute;de P\u0026uacute;blica\u003c/em\u003e. 2020;54:08. doi:https://doi.org/10.11606/s1518-8787.2020054001458\u003c/li\u003e\n\u003cli\u003eFurberg JK, Rasmussen S, Andersen PK, Ravn H. Methodological challenges in the analysis of recurrent events for randomised controlled trials with application to cardiovascular events in LEADER. \u003cem\u003ePharmaceutical Statistics\u003c/em\u003e. 2022;21(1):241-267. doi:10.1002/pst.2167\u003c/li\u003e\n\u003cli\u003eStrasser ZH, Greifer N, Hadavand A, Murphy SN, Estiri H. Estimates of SARS-CoV-2 Omicron BA.2 Subvariant Severity in New England. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. 2022;5(10):e2238354. doi:10.1001/jamanetworkopen.2022.38354\u003c/li\u003e\n\u003cli\u003eGhosh D, Lin DY. Nonparametric analysis of recurrent events and death. \u003cem\u003eBiometrics\u003c/em\u003e. 2000;56(2):554-562. doi:10.1111/j.0006-341x.2000.00554.x\u003c/li\u003e\n\u003cli\u003eFurberg JK, Rasmussen S, Andersen PK, Ravn H. Methodological challenges in the analysis of recurrent events for randomised controlled trials with application to cardiovascular events in LEADER. \u003cem\u003ePharmaceutical Statistics\u003c/em\u003e. 2022;21(1):241-267. doi:10.1002/pst.2167\u003c/li\u003e\n\u003cli\u003eZhuchkova S, Rotmistrov A. How to choose an approach to handling missing categorical data: (un)expected findings from a simulated statistical experiment. \u003cem\u003eQual Quant\u003c/em\u003e. 2022;56(1):1-22. doi:10.1007/s11135-021-01114-w\u003c/li\u003e\n\u003cli\u003eSimpson SJ, Du Berry C, Evans DJ, et al. Unravelling the respiratory health path across the lifespan for survivors of preterm birth. \u003cem\u003eThe Lancet Respiratory Medicine\u003c/em\u003e. 2024;12(2):167-180. doi:10.1016/S2213-2600(23)00272-2\u003c/li\u003e\n\u003cli\u003eTrusinska D, Zin ST, Sandoval E, Homaira N, Shi T. Risk Factors for Poor Outcomes in Children Hospitalized With Virus-associated Acute Lower Respiratory Infections: A Systematic Review and Meta-analysis. \u003cem\u003eThe Pediatric Infectious Disease Journal\u003c/em\u003e. 2024;43(5):467. doi:10.1097/INF.0000000000004258\u003c/li\u003e\n\u003cli\u003eCourse CW, Kotecha EA, Course K, Kotecha S. The respiratory consequences of preterm birth: from infancy to adulthood. \u003cem\u003eBr J Hosp Med\u003c/em\u003e. 2024;85(8):1-11. doi:10.12968/hmed.2024.0141\u003c/li\u003e\n\u003cli\u003ePriante E, Moschino L, Mardegan V, Manzoni P, Salvadori S, Baraldi E. Respiratory Outcome after Preterm Birth: A Long and Difficult Journey. \u003cem\u003eAmer J Perinatol\u003c/em\u003e. 2016;33(11):1040-1042. doi:10.1055/s-0036-1586172\u003c/li\u003e\n\u003cli\u003eKhashu M, Narayanan M, Bhargava S, Osiovich H. Perinatal Outcomes Associated With Preterm Birth at 33 to 36 Weeks\u0026rsquo; Gestation: A Population-Based Cohort Study. \u003cem\u003ePediatrics\u003c/em\u003e. 2009;123(1):109-113. doi:10.1542/peds.2007-3743\u003c/li\u003e\n\u003cli\u003eLaw BJ, Langley JM, Allen U, et al. The Pediatric Investigators Collaborative Network on Infections in Canada study of predictors of hospitalization for respiratory syncytial virus infection for infants born at 33 through 35 completed weeks of gestation. \u003cem\u003ePediatr Infect Dis J\u003c/em\u003e. 2004;23(9):806-814. doi:10.1097/01.inf.0000137568.71589.bd\u003c/li\u003e\n\u003cli\u003eHorn SD, Smout RJ. Effect of prematurity on respiratory syncytial virus hospital resource use and outcomes. \u003cem\u003eJ Pediatr\u003c/em\u003e. 2003;143(5 Suppl):S133-141. doi:10.1067/s0022-3476(03)00509-2\u003c/li\u003e\n\u003cli\u003eWillson DF, Landrigan CP, Horn SD, Smout RJ. Complications in infants hospitalized for bronchiolitis or respiratory syncytial virus pneumonia. \u003cem\u003eJ Pediatr\u003c/em\u003e. 2003;143(5 Suppl):S142-149. doi:10.1067/s0022-3476(03)00514-6\u003c/li\u003e\n\u003cli\u003eKenmoe S, Kengne-Nde C, Modiyinji AF, Rosa GL, Njouom R. Comparison of health care resource utilization among preterm and term infants hospitalized with Human Respiratory Syncytial Virus infections: A systematic review and meta-analysis of retrospective cohort studies. \u003cem\u003ePLOS ONE\u003c/em\u003e. 2020;15(2):e0229357. doi:10.1371/journal.pone.0229357\u003c/li\u003e\n\u003cli\u003eAhmed AM, Grandi SM, Pullenayegum E, et al. Short-Term and Long-Term Mortality Risk After Preterm Birth. \u003cem\u003eJAMA Network Open\u003c/em\u003e. 2024;7(11):e2445871. doi:10.1001/jamanetworkopen.2024.45871\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBaseline characteristics of singleton live births\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003cbr\u003e\u0026nbsp;N = 2,951,097\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003cbr\u003e\u0026nbsp;N = 288,466\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003cbr\u003e\u0026nbsp;N = 3,239,563\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge mother - group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e10-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e323,620 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e40,021 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e363,641 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e18-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,209,107 (41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e109,157 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,318,264 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e25-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e680,729 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e59,144 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e739,873 (22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e30-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e448,221 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e44,348 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e492,569 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e289,420 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e35,796 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e325,216 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge mother \u0026ndash; years, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e24 (20, 29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e24 (19, 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e24 (20, 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of schooling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e15,944 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,776 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e17,720 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1 to 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e85,533 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9,378 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e94,911 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e4 to 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e678,241 (23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e70,407 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e748,648 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e8 to 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,963,752 (66.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e185,960 (64.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,149,712 (66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026ge;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e190,005 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e18,847 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e208,852 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e17,622 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,098 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e19,720 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/\u003c/strong\u003e\u003cstrong\u003eethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,013,408 (34.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e101,184 (35.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,114,592 (34.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e244,639 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e24,266 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e268,905 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e15,278 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,346 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e16,624 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,624,497 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e155,856 (54.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,780,353 (55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9,014 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e901 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9,915 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e44,261 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e4,913 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e49,174 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e32,610 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3,530 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e36,140 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e579,969 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e54,789 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e634,758 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,654,045 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e164,197 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,818,242 (56.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eStable union\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e664,496 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e63,822 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e728,318 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e4,795 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e488 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5,283 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e15,182 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,640 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e16,822 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographic Region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e201,785 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e18,231 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e220,016 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e545,173 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e60,329 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e605,502 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,429,189 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e138,097 (47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,567,286 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e579,067 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e56,127 (19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e635,194 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eCentral-west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e195,883 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e15,682 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e211,565 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeprivation Index-City\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e1 (lowest deprivation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e623,404 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e61,203 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e684,607 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e663,452 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e64,127 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e727,579 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e787,349 (26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e73,686 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e861,035 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e556,922 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e53,770 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e610,692 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e5 (highest deprivation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e319,970 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e35,680 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e355,650 (11.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of prenatal appointments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e65,145 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e15,044 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e80,189 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e1 to 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e228,960 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e50,061 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e279,021 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e4 to 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e771,292 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e110,783 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e882,075 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026ge;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,858,575 (63.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e107,185 (37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,965,760 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e27,125 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5,393 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e32,518 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdequate number of prenatal appointments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,231,188 (76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e174,536 (61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,405,724 (75.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of previous pregnancies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,866,754 (63.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e174,366 (60.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,041,120 (64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,022,395 (34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e108,326 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,130,721 (35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e61,948 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e5,774 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e67,722 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevious fetal loss\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e509,346 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e61,009 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e570,355 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e123,505 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e10,403 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e133,908 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational age, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e39.00 (38.00 - 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e35.00 (33.00 - 36.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e39.00 (38.00 - 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational age method\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003ePhysical Exam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,598,288 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e155,498 (53.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,753,786 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eUltrasonography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,352,809 (45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e132,968 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,485,777 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003emoderate to late preterm (32 to 37 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e243,451 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e243,451 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003every preterm (28 to less than 32 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e30,246 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e30,246 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003eextremely preterm (less than 28 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e14,769 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e14,769 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex of live birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,509,774 (51.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e152,266 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,662,040 (51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,441,323 (48.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e136,200 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e1,577,523 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of birth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e127,493 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e12,766 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e140,259 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e284,001 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e28,911 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e312,912 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e383,701 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e37,331 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e421,032 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e474,401 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e45,161 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e519,562 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e490,522 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e46,295 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e536,817 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e402,268 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e39,175 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e441,443 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e416,982 (14.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e41,562 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e458,544 (14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e371,729 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e37,265 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e408,994 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth weight (g), median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3,250 (2,970, 3,550)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,350 (1,860, 2,722)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e3,205 (2,890, 3,520)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow birth weight (\u0026lt;2500g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e112,679 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e173,389 (60.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e286,068 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight for gestational age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eSGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e216,159 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e28,214 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e244,373 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,351,054 (79.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e222,572 (77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,573,626 (79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e383,884 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e37,680 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e421,564 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCongenital Anomaly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e25,078 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e7,251 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e32,329 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e17,797 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e2,520 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e20,317 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelayed antenatal care\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e697,108 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e70,600 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e767,708 (23.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e190,268 (6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e32,064 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e222,332 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eApgar 5\u0026apos;, median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9.00 (9.00, 10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9.00 (8.00, 10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e9.00 (9.00, 10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e33,940 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e4,674 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e38,614 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow Apgar \u0026lt;7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e21,537 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e16,034 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e37,571 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31px;\"\u003e\n \u003cp\u003eMissing data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e33,940 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e4,674 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 22px;\"\u003e\n \u003cp\u003e38,614 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eIQR: Interquartile range; Weight for gestational age was defined based on intergrowth charts and comprised: (1) small for gestational age (SGA) \u0026ndash; i.e. birth weight \u0026lt;10th percentile for sex and gestational age; (2) Appropriate for gestational age (AGA) \u0026ndash; i.e. birthweight between 10th and 90th percentiles for sex and gestational age; (3) Large for gestational age (LGA) \u0026ndash; i.e. birthweight \u0026gt; 90th percentile for sex and gestational age.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Mean ratios for the number of respiratory-related hospitalisations and hazard ratios for respiratory-related mortality and all-cause mortality comparing preterm and term children.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"234%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. children\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePerson years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo respiratory hospitalisations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Ratio (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. respiratory deaths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard Ratio - Respiratory Death (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo. deaths\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard Ratio - All cause mortality (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2951097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e9441592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e292856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e18357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e288466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e854531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e40072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.40 (1.38 to 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.95 (3.62 to 4.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e22485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e11.99 (11.74 to 12.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eModerate to late\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e243453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e753574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e31814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.32 (1.30 to 1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.74 (2.48 to 3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e8001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.92 (4.79 to 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eVery\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e30246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e80174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e5892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.97 (1.89 to 2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e10.76 (9.29 to 12.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e5904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e31.93 (30.95 to 32.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtremely\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e14767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e20783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.60 (1.49 to 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e21.31 (17.42 to 26.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e8580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e146.62 (142.41 to 150.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0-27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2951097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e216671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e11504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e7079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e288466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e20196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.41 (1.34 to 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.69 (2.46 to 5.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e17000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e22.95 (22.28 to 23.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cem\u003eModerate to late\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e243453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e17617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.41 (1.33 to 1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.61 (1.61 to 4.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e4917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e7.75 (7.47 to 8.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cem\u003eVery\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e30246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e1957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.48 (1.27 to 1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e4540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e59.21 (56.90 to 61.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtremely\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e14767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.37 (1.09 to 1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e7543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e267.53 (258.05 to 277.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e28-90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2915024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e497049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e44869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e3308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e268609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e45518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e7056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.68 (1.63 to 1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.66 (4.00 to 5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e2794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e8.23 (7.81 to 8.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eModerate to late\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e236067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e40130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e5885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.58 (1.54 to 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.18 (2.65 to 3.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e1328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.45 (4.17 to 4.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eVery\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e25413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e4252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e2.26 (2.11 to 2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e13.00 (10.08 to 16.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e23.82 (21.97 to 25.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtremely\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e7129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e1136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e2.62 (2.32 to 2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e25.83 (18.28 to 36.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e82.12 (75.42 to 89.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e91-365\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2847010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2025405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e113189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e4206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e259830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e183921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e17174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.65 (1.62 to 1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.58 (4.02 to 5.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e1905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.52 (4.28 to 4.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eModerate to late\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e229434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e162521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e13135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.43 (1.40 to 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.94 (2.50 to 3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e1176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.17 (2.96 to 3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eVery\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e24106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e17012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3.02 (2.88 to 3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e14.84 (12.06 to 18.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e11.56 (10.48 to 12.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtremely\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e6290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e4389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e4.58 (4.24 to 4.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e25.44 (18.77 to 34.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e27.20 (24.01 to 30.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2532640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2326565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e66898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e1924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e228725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e209739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e7992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.34 (1.30 to 1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.46 (1.95 to 3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.53 (2.28 to 2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eModerate to late\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e202207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e185335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e6322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.20 (1.16 to 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.01 (1.54 to 2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.05 (1.83 to 2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eVery\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e21091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e19414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e2.08 (1.93 to 2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.51 (2.73 to 7.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.69 (3.76 to 5.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtremely\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e5427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e4990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e3.63 (3.22 to 4.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e11.06 (5.88 to 20.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e11.54 (8.74 to 15.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2116767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e1919681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e31069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e190217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e172584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e3518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.26 (1.21 to 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.12 (2.14 to 4.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.74 (1.46 to 2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eModerate to late\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e167962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e152191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.16 (1.11 to 1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.75 (1.81 to 4.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.45 (1.19 to 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eVery\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e17706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e16222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.81 (1.62 to 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.66 (2.56 to 5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtremely\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e4549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e4171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e2.87 (2.39 to 3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.56 (2.45 to 8.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e1708772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e1469740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e16621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e153254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e132408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.21 (1.14 to 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.79 (1.44 to 2.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eModerate to late\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e134932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e116568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e1493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.14 (1.08 to 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.55 (1.21 to 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eVery\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e14565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e12591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.52 (1.28 to 1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e3.12 (1.92 to 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtremely\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e3757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e3249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e2.38 (1.83 to 3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e1219541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e986481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e8706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2px;\"\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 7px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreterm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e110538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e90165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.18 (1.10 to 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.78 (1.34 to 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eModerate to late\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e97233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e79213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.12 (1.03 to 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e1.81 (1.35 to 2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eVery\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e10594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e8727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.59 (1.29 to 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cem\u003eExtremely\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 3px;\"\u003e\n \u003cp\u003e2225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 4px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\n \u003cp\u003e1.82 (1.19 to 2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 57px;\"\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\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eN/A: To ensure reliable estimates, only periods with at least 10 events in each group were estimated.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7509730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7509730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Preterm birth and respiratory diseases disproportionately affect low-and middle-income countries (LMICs). Although preterm birth is a major contributor to the burden of respiratory morbimortality in early childhood, most evidence comes from high-income settings. To address this gap, we examined respiratory-related hospitalisations and deaths among preterm children in Brazil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We conducted a population-based cohort study using the CIDACS Birth Cohort, including all live births in Brazil from January 1, 2011, to November 30, 2018. Preterm infants were defined as infants born before 37 weeks of gestation. We examined respiratory-related hospital admissions and deaths in children under five. Mean ratios (MR) and 95% confidence intervals (CI) were estimated using the Ghosh-Lin model; hazard ratios (HR) were estimated using Cox models. Maternal characteristics were adjusted through inverse probability weighting, with treatment probabilities estimated via entropy balancing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The study included 3,239,563 live births, with 288,466 (8.9%) classified as preterm. The MR for under-five respiratory hospitalisation, comparing preterm to term births, was 1.40 (95%CI:1.38–1.42), peaking at 1.68 (1.63–1.72) between 28 and 90 days, declining to approximately 1.18 (1.10-1.28) at the fourth year. For respiratory disease deaths, the under-five HR was 3.94 (3.62–4.30). Respiratory-related mortality was highest between 28-90 days of age, with an HR of 4.66 (4.00–5.43), decreasing to 1.25 (0.62–2.51) by three years of age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003ePreterm newborns have a higher risk of respiratory illness than full-term children, particularly in their first year. This understanding can guide health strategies to address premature birth issues by identifying important periods of vulnerability.\u003c/p\u003e","manuscriptTitle":"Nationwide Study of Respiratory-Related Hospitalisations and Deaths in Preterm Children in Brazil: A Registry-based Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-12 15:00:58","doi":"10.21203/rs.3.rs-7509730/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-28T10:32:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T23:40:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T01:30:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186494109426414003985995994139972722482","date":"2025-09-08T19:18:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244006257611884905177929925456560473762","date":"2025-09-08T18:21:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-06T21:22:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T16:36:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T07:36:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Respiratory Research","date":"2025-09-01T14:49:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"respiratory-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rere","sideBox":"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)","snPcode":"12931","submissionUrl":"https://submission.nature.com/new-submission/12931/3","title":"Respiratory Research","twitterHandle":"@RespiratoryBMC","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6f04205-8af3-42b6-a502-a159a1f8fdf0","owner":[],"postedDate":"September 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:07:45+00:00","versionOfRecord":{"articleIdentity":"rs-7509730","link":"https://doi.org/10.1186/s12931-025-03449-6","journal":{"identity":"respiratory-research","isVorOnly":false,"title":"Respiratory Research"},"publishedOn":"2025-12-13 15:58:32","publishedOnDateReadable":"December 13th, 2025"},"versionCreatedAt":"2025-09-12 15:00:58","video":"","vorDoi":"10.1186/s12931-025-03449-6","vorDoiUrl":"https://doi.org/10.1186/s12931-025-03449-6","workflowStages":[]},"version":"v1","identity":"rs-7509730","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7509730","identity":"rs-7509730","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}