BackgroundPreterm birth is a major cause of prenatal and postnatal mortality particularly in developing countries. This study investigated the maternal risk factors associated with the risk of preterm birth.MethodsA population-based case-control study was conducted in several provinces of Iran on 2463 mothers referred to health care centers. Appropriate descriptive and analytical statistical methods were used to evaluate the association between maternal risk factors and the risk of preterm birth. All tests were two-sided, and P values < 0.05 were considered to be statistically significant.ResultsThe mean gestational age was 31.5 ± 4.03 vs. 38.8 ± 1.06 weeks in the case and control groups, respectively. Multivariate regression analysis showed a statistically significant association between preterm birth and mother’s age and ethnicity. Women of Balooch ethnicity and age ≥ 35 years were significantly more likely to develop preterm birth (OR: 1.64; 95% CI: 1.01–-2.44 and OR: 9.72; 95% CI: 3.07–30.78, respectively). However, no statistically significant association was observed between preterm birth and mother’s place of residence, level of education, past history of cesarean section, and BMI.ConclusionDespite technological advances in the health care system, preterm birth still remains a major concern for health officials. Providing appropriate perinatal health care services as well as raising the awareness of pregnant women, especially for high-risk groups, can reduce the proportion of preventable preterm births.

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Objectives: Reducing infant mortality in the whole world is one of the millennium development goals.The aim of this study was to determine the factors related to infant mortality using data mining algorithms. Methods: This population-based case-control study was conducted in eight provinces of Iran. A sum of 2,386 mothers (1,076 cases and 1,310 controls) enrolled in this study. Data were extracted from health records of mothers and filled with checklists in health centers. We employed several data mining algorithms such as AdaBoost classifier, Support Vector Machine, Artificial Neural Networks, Random Forests, K-nearest neighborhood, and Naïve Bayes in order to recognize the important predictors of infant death; binary logistic regression model was used to clarify the role of each selected predictor. Results: In this study, 58.7% of infant mortalities occurred in rural areas, that 55.6% of them were boys. Moreover, Naïve Bayes and Random Forest were highly capable of predicting related factors among data mining models. Also, the results showed that events during pregnancy such as dental disorders, high blood pressure, loss of parents, factors related to infants such as low birth weight, and factors related to mothers like consanguineous marriage and gap of pregnancy (< 3 years) were all risk factors while the age of pregnancy (18 - 35 year) and a high degree of education were protective factors. Conclusions: Infant mortality is the consequence of a variety of factors, including factors related to infants themselves and their mothers and events during pregnancy. Owing to the high accuracy and ability of modern modeling compared to traditional modeling, it is recommended to use machine learning tools for indicating risk factors of infant mortality.

Background: Undesirable health outcomes of anemia impact all different groups of people within a society especially pregnant women (PW). Objectives: This study aimed to evaluate other fetal and maternal complications of anemia in various trimesters of pregnancy. Methods: A large retrospective cohort study was conducted on Iranian PW in 2017. The first and third trimesters of pregnancy were assessed as a separate study. The first study included 1038 anemic and 2463 non-anemic PW and the second comprised 756 anemic and 1986 non-anemic PW. The outcome-related pregnancies were analyzed for each study. Results: After adjusting for the potential confounding factors, the odds of neonatal mortality (OR = 1.63; CI 95%, 1.25 -2.13) were significantly higher and the odds of cesarean delivery (OR = 0.6; CI 95%, 0.46 -0.75) were significantly lower in women who had anemia during the first trimester. The chance of pre-term delivery (< 37 weeks; OR = 2.15; CI 95%, 1.6 -2.91) and abortion (OR = 1.68; CI 95%, 1.11 -2.53) was significantly higher in women who had anemia during the third trimester, while the chance of low birth weight (< 2500 kg) (OR = 0.66; CI 95%, 0.46 -0.93) was lower in anemic women during the third trimester than in those without anemia. Conclusions: Pregnant women who experience anemia in both first and third trimesters of pregnancy have different unpleasant pregnancy outcomes. Since anemia is preventable during pregnancy, many of these outcomes such as neonatal mortality, low birth weight, preterm and cesarean delivery, and abortion could be prevented and decreased by providing health education before pregnancy.