Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis

Background Few studies on cluster-based synthetic effects of multiple risk factors for birth defects have been reported. The present study aimed to identify maternal exposure clusters, explore the association between clusters of risk factors and birth defects, and further screen women with high risk for birth defects among expectant mothers. Methods Data were drawn from a large-scale, retrospective epidemiological survey of birth defects from 2006 to 2008 in six counties of Shanxi Province, China, using a three-level stratified random cluster sampling technique. Overall risk factors were extracted using eight synthetic variables summed and examined as a total risk factor score: maternal delivery age, genetic factors, medical history, nutrition and folic acid deficiency, maternal illness in pregnancy, drug use in pregnancy, environmental risk factors in pregnancy, and unhealthy maternal lifestyle in pregnancy. Latent class cluster analysis was used to identify maternal exposure clusters based on these synthetic variables. Adjusted odds ratios (AOR) were used to explore associations between clusters and birth defects, after adjusting for confounding variables using logistic regression. Results Three latent maternal exposure clusters were identified: a high-risk (6.15 %), a moderate-risk (22.39 %), and a low-risk (71.46 %) cluster. The prevalence of birth defects was 14.08 %, 0.85 %, and 0.52 % for the high-, middle- and low-risk clusters respectively. After adjusting for maternal demographic variables, women in the high-risk cluster were nearly 31 times (AOR: 30.61, 95 % CI: [24.87, 37.67]) more likely to have an infant with birth defects than low-risk women. Conclusions A high-risk group of mothers in an area with a high risk for birth defects were screened in our study. Targeted interventions should be conducted with women of reproductive age to improve neonatal birth outcomes in areas with a high risk of birth defects.