Propensity Score-Matched Analysis on the Association Between Pregnancy Infections and Adverse Birth Outcomes in Rural Northwestern China

Sci Rep. 2018 Mar 26;8(1):5154. doi: 10.1038/s41598-018-23306-5.

Abstract

The purpose of this study is to examine the relationship between infections and birth outcomes in pregnant Chinese women by using propensity score (PS) matching. The data used here was from a large population-based cross-sectional epidemiological survey on birth defects in Shaanxi province, Northwest China. The babies born during 2010-2013 and their mothers were selected with a stratified multistage sampling method. We used PS-matched (1:1) analysis to match participants with infections to participants without infections. Of 22916 rural participants, the overall prevalence of infection was about 39.96%. 5381 pairs were matched. We observed increased risks of birth defects with infections, respiratory infections and genitourinary infections during the pregnancy (OR, 1.59; 95% CI: 1.21-2.08; OR, 1.44; 95% CI: 1.10-1.87; OR, 3.11; 95% CI: 1.75-5.54). There was also a significant increase of low birth weight associated with respiratory infections (1.13(1.01-1.27)). The association of birth defect with the infection could be relatively stable but the effect could be mediated by some important factors such as mother's age, education level and economic level. The infection during pregnancy is common in Chinese women and might increase the risk of offspring birth defects and low birth weight, especially in younger, lower education, poor pregnant women.

Publication types

  • Observational Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Age Factors
  • China / epidemiology
  • Congenital Abnormalities / etiology
  • Cross-Sectional Studies
  • Female
  • Humans
  • Infant, Low Birth Weight
  • Infant, Newborn
  • Live Birth
  • Logistic Models
  • Pregnancy
  • Pregnancy Complications, Infectious / epidemiology*
  • Pregnancy Outcome / epidemiology*
  • Prevalence
  • Propensity Score*
  • Risk Factors
  • Rural Health*
  • Rural Population
  • Socioeconomic Factors
  • Statistics, Nonparametric