Methods From January 1, 2014 to December 31, 2019, a total of 79 173 premature infants who were delivered in Beijing medical institutions and whose demographic information had been included into Maternal and Child Health Care Network Information System in Beijing were selected as research subjects. According to gestational age<28 weeks, ≥ 28-32 weeks, ≥ 32-34 weeks, and ≥ 35-37 weeks, there were 1 021 cases of extremly early preterm infants (EEPI), 7 858 cases of early preterm infants (EPI), 9 102 cases of medium preterm infants (MPI), and 61 192 cases of late preterm infants (LPI), respectively. Demographic information of all premature infants (registered residence, gender, and their parental age, education level, occupation), birth age, single or multiple pregnancy, maternal risk during pregnancy (high risk or low risk), and perinatal outcome of premature infants were collected. Chi-square test was used to analyze the incidence of premature infants, univariate analysis of influencing factors of EEPI, EPI, MPI, LPI and their perinatal outcomes. Multivariate and ordinal logistic regression analysis was used to analyze influencing factors of EEPI, EPI, MPI, LPI. Birth weight of EEPI, EPI, MPI, LPI were compared by one-way ANOVA. The procedure followed in this study was in accordance with the standards formulated by Ethics Committee of Beijing Obstetrics and Gynecology Hospital, Capital Medical University and approved by the Ethics Committee (Approval No. IEC-C-03-V04-FJ2).
Results ①From 2014 to 2019, the overall incidence of premature infants was 5.68% (79 173/ 1 394 782). The incidence of premature infants in every gear of 2014 to 2019 was 4.55% (11 355/ 249 429), 4.56% (9 549/209 455), 6.67% (15 983/239 692), 5.56% (14 674/263 991), 6.45% (13 790/213 819) and 6.33% (13 822/218 396), respectively, showing an overall increasing trend, and the difference was statistically significant (χ2=1 936.451, P<0.001). The incidence of EEPI, EPI, MPI, LPI also showed an increasing trend, and all differences were statistically significant (χ2=102.991, 244.086, 242.817, 1 381.002; P<0.001). ②Univariate analysis of influencing factors of EEPI, EPI, MPI and LPI showed that there statistically significant differences in constituent ratios of registered residence, mother′s age, mother′s education level, maternal risk during pregnancy, father′s age, father′s education level and father′s occupation in EEPI, EPI, MPI and LPI (P<0.05). Multivariate and ordinal logistic regression analysis showed that maternal risk during pregnancy, father′s occupation and education level were independent influencing factors of EEPI, EPI, MPI, and LPI. Pregnant mothers who were at low risk of pregnancy were 1.049 times more likely to deliver a premature infant with later term than those who were at high risk of pregnancy (OR=1.049, 95%CI: 1.001-1.100, P=0.047). Fathers who were civil servants, military personnel, and state-owned enterprises and public institutions employees were 1.351 times more likely to obtain a premature infant with later term than those who were unemployed or students (OR=1.351, 95%CI: 1.290-1.415, P<0.001). Fathers who were employees of private enterprises, private enterprises or self-employed were 1.293 times more likely to obtain a premature infant with later term than those who were unemployed or students (OR=1.293, 95%CI: 1.239-1.351, P<0.001). Fathers who had an education level of post-graduate or above were 1.084 times more likely to obtain a premature infant with later term than those who had an education level of high school and below (OR=1.084, 95%CI: 1.000-1.176, P=0.049). ③The birth weight of male, non-Beijing registered residence and single pregnancy premature infants were (2 455.5±601.2) g, (2 420.1±605.9) g and (2 456.8±612.4) g, respectively, which were significantly higher than those of female, Beijing registered residence and multiple pregnancy premature infants [(2 347.5±593.3) g, (2 400.1±596.6) g and (2 223.8±504.2) g], and all differences were statistically significant (t=5.375, 4.715, 709.884; P=0.020, 0.030, <0.001). There was statistical difference in birth weight of premature infants born in 2014 to 2019 (F=19.912, P<0.001). ④The incidence of congenital malformation, intracranial hemorrhage, birth asphyxia, abnormal hearing screening and genetic metabolic diseases were 0.43% (343/79 173), 0.21% (167/79 173), 6.45% (5 105/79 173), 2.34% (1 809/77 236) and 0.10% (78/79 173), respectively. There were statistically significant differences in incidence of congenital malformation, intracranial hemorrhage and birth asphyxia, and constituent ratio of neonatal hearing screening pass, fail and non-screened among EEPI, EPI, MPI and LPI (χ2=140.208, 25.281, 9 656.282, 197.692; P<0.001).