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Background: Previous studies have shown the interaction between age and socioeconomic status (SES) on the risk of infertility in the UK, but the association is still unclear in the United States. Therefore, the present study investigated the effect of age on the relationship between SES and the risk of infertility in American women., Methods: The study included adults who participated in the National Health and Nutrition Examination Survey (NHANES) from 2013 to 2018. The poverty income ratio (PIR) was used to represent the SES of the population. With participants stratified according to age category (< 35 years; ≥ 35 years), we further assessed differences in the relationship between PIR and infertility risk among participants of different age groups using multivariate logistic regression and interaction tests., Results: Approximately 3,273 participants were enrolled in the study. There were 399 cases of infertility and 2,874 cases without infertility. In women ≥ 35 years of age, PIR levels were significantly higher in infertile participants than in non-infertile participants, but no such difference was found in those < 35 years of age. The association of PIR with the risk of infertility appeared to differ between age < 35 years and age ≥ 35 years (OR: 0.99, 95%Cl: 0.86-1.13 vs. OR: 1.24, 95%Cl: 1.12-1.39) in a fully adjusted model. Furthermore, an interaction between age and PIR increased the risk of infertility (p-value for interaction < 0.001)., Conclusion: Our study found that age may influence the association between PIR and infertility. It is imperative to perform further studies to provide more evidence., (© 2023. The Author(s).)

Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 concentration is a critical step for understanding the pollution problem and embarking on the necessary remedy. This research designs, implements and compares two inverse distance weighting (IDW)-based spatiotemporal interpolation methods, in order to assess the trend of daily PM2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously, using the so-called extension approach. Time values are calculated with the help of a factor under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. Various IDW-based spatiotemporal interpolation methods with different parameter configurations are evaluated by cross-validation. In addition, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure, named k-d tree, are adapted in this paper to address the computational challenges. Significant computational improvement has been achieved. Finally, a web-based spatiotemporal IDW-based interpolation application is designed and implemented where users can visualize and animate spatiotemporal interpolation results.