A comparative analysis of Postpartum Hemorrhage incidence and influencing factors between nulliparous and multiparous women in Hunan Province, China: A multicenter retrospective cohort study.

Objectives: Postpartum hemorrhage (PPH) is a common cause of maternal death worldwide, but data on PPH incidence and influencing factors for nulliparous and multiparous women is scarce. So, the study aimed to assess the differences in PPH incidence and influencing factors between nulliparous and multiparous women. Methods: A multicenter retrospective cohort study was conducted among women who gave birth at ≥ 28 weeks of gestation in Hunan Province, China, from January 2017 to December 2018. Logistic regression assessed PPH-influencing factors, and the receiver operating characteristic curve (ROC curve) assessed the predictive performance of identified factors. Results: A total of 144,845 postpartum women were included in the study. The incidence of PPH (blood loss ≥ 500 ml) was 2.1 % and 1.7 % for nulliparous and multiparous women, respectively. Among the nulliparous and multiparous women, similar influencing factors of PPH included erythrocyte suspension transfusion before childbirth, anemia, soft-birth canal avulsion, Cesarean-section, placenta abruption, and general anesthesia administration before birth. Thrombophlebitis was associated [aOR 18.46(1.67-20.31)] with PPH among only the nulliparous women, while instrument-assisted birth [aOR 1.95(1.16-3.28)] and gestational hypertension [aOR 1.57(1.13-2.19)] were associated with PPH among only the multiparous women. The areas under the ROC-curve for the overall-cohort, nulliparous, and multiparous groups were [0.829(0.821-0.838)], [0.828(0.815-0.840)] and [0.833(0.822-0.844)], respectively. Conclusion: PPH incidence is higher among nulliparous women than among multiparous women, but influencing factors vary relatively by parity. The study findings provide new insights into the use of different approaches to PPH prevention for nulliparous and multiparous women in clinical practice.

Epidemiological Survey of Hemoglobinopathies Based on Next-Generation Sequencing Platform in Hunan Province, China.

Objective: This study was aimed at investigating the carrier rate of, and molecular variation in, α- and ß-globin gene mutations in Hunan Province. Methods: We recruited 25,946 individuals attending premarital screening from 42 districts and counties in all 14 cities of Hunan Province. Hematological screening was performed, and molecular parameters were assessed. Results: The overall carrier rate of thalassemia was 7.1%, including 4.83% for α-thalassemia, 2.15% for ß-thalassemia, and 0.12% for both α- and ß-thalassemia. The highest carrier rate of thalassemia was in Yongzhou (14.57%). The most abundant genotype of α-thalassemia and ß-thalassemia was -α 3.7/αα (50.23%) and ß IVS-II-654/ß N (28.23%), respectively. Four α-globin mutations [CD108 (ACC>AAC), CAP +29 (G>C), Hb Agrinio and Hb Cervantes] and six ß-globin mutations [CAP +8 (C>T), IVS-II-848 (C>T), -56 (G>C), beta nt-77 (G>C), codon 20/21 (-TGGA) and Hb Knossos] had not previously been identified in China. Furthermore, this study provides the first report of the carrier rates of abnormal hemoglobin variants and α-globin triplication in Hunan Province, which were 0.49% and 1.99%, respectively. Conclusion: Our study demonstrates the high complexity and diversity of thalassemia gene mutations in the Hunan population. The results should facilitate genetic counselling and the prevention of severe thalassemia in this region.

[Using multivariate multilevel model in studying the influential factors of violence among rural high school students in Changsha].

OBJECTIVE: To provide evidence for setting up violence intervention programs in rural middle schools, through studying the influential factors. METHODS: Taking variables including emotional, physical and sexual violence in the past year as the multi-dependent variables before multivariate multilevel model logistic regression model was adopted to analyze the correlations among the three kinds of violence and the influential factors. RESULTS: Among 3620 respondents, the incidence rates of emotional, physical and sexual violence weres 21.5%, 24.3% and 2.0% respectively. The correlation coefficients between emotional violence vs. physical violence, emotional violence vs. sexual violence, physical violence vs. sexual violence were 0.337, 0.133, 0.131 respectively when the random effect of class difference was separated by multivariate multilevel model. There was an internal aggregation of the incidence rate on physical violence in different grades (chi2=4.286, P = 0.038) and an internal relevant between emotional violence vs. sexual violence (chi2 = 4.239, P = 0.039), physical violence vs. sexual violence (chi2 = 4.482, P = 0.034). The influential factors on the incidence rates of violence would include: sex, smoking status, family without harmony, tendency of bullying others and the level on self-esteem etc. CONCLUSION: When the random effect of class difference was separated by multivariate multilevel model, the estimated results would be more precise. Other than paying more attention to both individual and family influential factors when taking measures to reduce the incidence rate of violence in high school students, the effect of environment in the class should not be ignored.

[Risk prediction model of perinatal congenital heart disease].

Through analyzing the influencing factors of congenital heart disease (CHD), it is aimed to establish CHD risk prediction model in fetus, and simultaneously provide theoretical foundation for CHD prevention. One-factor logistic regression method was used to screen the significant factors regarding CHD, and to separately adopt multiple-factor non-conditional logistic regression method and decision tree to set up model prediction fetus CHD risk and to analyze the advantages and shortcomings. Correct classification rates turned to be 80.93% and 82.79% respectively among 215 'training samples' by the two methods and the rates were 85.45% and 89.09% respectively among 55 'testing samples'. The alliance of logistic regression and decision tree can overcome influence by co-linearity to guarantee the accuracy and perfection, as well as promoting the predictive accuracy.