Multivariate logistic regression analysis of risk factors for birth defects: a study from population-based surveillance data

Abstract

Objective

To explore risk factors for birth defects (including a broad range of specific defects).

Methods

Data were derived from the Population-based Birth Defects Surveillance System in Hunan Province, China, 2014–2020. The surveillance population included all live births, stillbirths, infant deaths, and legal termination of pregnancy between 28 weeks gestation and 42 days postpartum. The prevalence of birth defects (number of birth defects per 1000 infants) and its 95% confidence interval (CI) were calculated. Multivariate logistic regression analysis (method: Forward, Wald, α = 0.05) and adjusted odds ratios (ORs) were used to identify risk factors for birth defects. We used the presence or absence of birth defects (or specific defects) as the dependent variable, and eight variables (sex, residence, number of births, paternal age, maternal age, number of pregnancies, parity, and maternal household registration) were entered as independent variables in multivariate logistic regression analysis.

Results

Our study included 143,118 infants, and 2984 birth defects were identified, with a prevalence of 20.85% (95%CI: 20.10–21.60). Multivariate logistic regression analyses showed that seven variables (except for parity) were associated with birth defects (or specific defects). There were five factors associated with the overall birth defects. The risk factors included males (OR = 1.49, 95%CI: 1.39–1.61), multiple births (OR = 1.44, 95%CI: 1.18–1.76), paternal age < 20 (OR = 2.20, 95%CI: 1.19–4.09) or 20–24 (OR = 1.66, 95%CI: 1.42–1.94), maternal age 30–34 (OR = 1.16, 95%CI: 1.04–1.29) or > = 35 (OR = 1.56, 95%CI: 1.33–1.81), and maternal non-local household registration (OR = 2.96, 95%CI: 2.39–3.67). Some factors were associated with the specific defects. Males were risk factors for congenital metabolic disorders (OR = 3.86, 95%CI: 3.15–4.72), congenital limb defects (OR = 1.34, 95%CI: 1.14–1.58), and congenital kidney and urinary defects (OR = 2.35, 95%CI: 1.65–3.34). Rural areas were risk factors for congenital metabolic disorders (OR = 1.21, 95%CI: 1.01–1.44). Multiple births were risk factors for congenital heart defects (OR = 2.09, 95%CI: 1.55–2.82), congenital kidney and urinary defects (OR = 2.14, 95%CI: 1.05–4.37), and cleft lip and/or palate (OR = 2.85, 95%CI: 1.32–6.15). Paternal age < 20 was the risk factor for congenital limb defects (OR = 3.27, 95%CI: 1.10–9.71), 20–24 was the risk factor for congenital heart defects (OR = 1.64, 95%CI: 1.24–2.17), congenital metabolic disorders (OR = 1.56, 95%CI: 1.11–2.21), congenital limb defects (OR = 1.61, 95%CI: 1.14–2.29), and congenital ear defects (OR = 2.13, 95%CI: 1.17–3.89). Maternal age < 20 was the risk factor for cleft lip and/or palate (OR = 3.14, 95%CI: 1.24–7.95), 30–34 was the risk factor for congenital limb defects (OR = 1.37, 95%CI: 1.09–1.73), >=35 was the risk factor for congenital heart defects (OR = 1.51, 95%CI: 1.14–1.99), congenital limb defects (OR = 1.98, 95%CI: 1.41–2.78), and congenital ear defects (OR = 1.82, 95%CI: 1.06–3.10). Number of pregnancies = 2 was the risk factor for congenital nervous system defects (OR = 2.27, 95%CI: 1.19–4.32), >=4 was the risk factor for chromosomal abnormalities (OR = 2.03, 95%CI: 1.06–3.88) and congenital nervous system defects (OR = 3.03, 95%CI: 1.23–7.47). Maternal non-local household registration was the risk factor for congenital heart defects (OR = 3.57, 95%CI: 2.54–5.03), congenital metabolic disorders (OR = 1.89, 95%CI: 1.06–3.37), congenital limb defects (OR = 2.94, 95%CI: 1.86–4.66), and congenital ear defects (OR = 3.26, 95%CI: 1.60–6.65).

Conclusion

In summary, several risk factors were associated with birth defects (including a broad range of specific defects). One risk factor may be associated with several defects, and one defect may be associated with several risk factors. Future studies should examine the mechanisms. Our findings have significant public health implications as some factors are modifiable or avoidable, such as promoting childbirths at the appropriate age, improving the medical and socio-economic conditions of non-local household registration residents, and devoting more resources to some specific defects in high-risk groups, which may help reducing birth defects in China.