Reproductive History and Infertility Risk: Insights from the 2017–2020 National Health and Nutrition Examination Survey

Infertility is a significant public health issue in global women's health. As the trend of delayed childbearing among women increases, the associated risks have garnered growing attention. Previous studies have indicated a potential association between reproductive factors and infertility, although the precise nature of this relationship remains unclear. Therefore, investigating the relationship between reproductive factors and infertility is of considerable academic and practical significance. This study is based on data from the US National Health and Nutrition Examination Survey (NHANES) collected between 2017 and 2020, and includes 1,891 women. Multiple regression analyses and restricted cubic spline (RCS) curves were employed to evaluate the relationships of age at first birth (AFB), age at last birth (ALB), and number of live births (NLB) with infertility. Potential confounders such as age and race were controlled for in the analysis. The results indicated that increases in AFB and ALB were significantly associated with a heightened risk of infertility, whereas an increase in NLB was significantly linked to a reduced risk. Specifically, a J-shaped relationship was observed between AFB and infertility, a U-shaped association between ALB and infertility, and a linear negative correlation between NLB and infertility. Subgroup analysis revealed that living alone was significantly associated with infertility risk, with certain subgroups exhibiting a lower risk. This study elucidates the critical relationship between reproductive factors and the risk of infertility. Delays in AFB and ALB were found to significantly increase the risk of infertility, whereas an increase in NLB markedly reduced the risk. These findings provide a basis for the early screening and intervention of infertility, and offer scientific support for the evaluation of women's reproductive health and policy-making. Future research should further explore the underlying mechanisms of these factors across different populations and conduct in-depth analyses using longitudinal data.