A Random Effect Model Approach to Survey Data Integration

Combining information from several surveys, or survey integration, is an important practical problem in survey sampling. When the samples are selected from similar but different populations, random effect models can be used to describe the sample observations and to borrow strength from multiple surveys. In this paper, we consider a prediction approach to survey integration assuming random effect models. The sampling designs are allowed to be informative. The model parameters are estimated using a version of EM algorithm accounting for the sampling design. The mean squared error estimation is also discussed. Two limited simulation studies are used to investigate the performance of the proposed method

Differential trend of mild and severe preeclampsia among nulliparous women: a population-based study of South Korea

We explored the annual risks of mild and severe preeclampsia (PE) among nulliparous women. Using the National Health Information Database of South Korea, 1,317,944 nulliparous women who gave live births were identified. Mild PE increased from 0.9% in 2010 to 1.4% in 2019 (P for trend=0.006), while severe PE decreased from 0.4% in 2010 to 0.3% in 2019 (P=0.049). The incidence of all types of PE (mild and severe) showed no linear change (P=0.514). Adjusted odds ratio (OR) of severe PE decreased in 2013 (0.68; 95% confidence interval [CI]: 0.60, 0.77) and beyond compared to that in 2010, while the OR of mild PE increased in 2017 (1.14; 95% CI: 1.06, 1.22) and beyond. Mild PE was found to be less likely to progress to the severe form since 2010; however, the overall risk of PE among women did not change

A Random Effect Model Approach to Survey Data Integration

Combining information from several surveys, or survey integration, is an important practical problem in survey sampling. When the samples are selected from similar but different populations, random effect models can be used to describe the sample observations and to borrow strength from multiple surveys. In this paper, we consider a prediction approach to survey integration assuming random effect models. The sampling designs are allowed to be informative. The model parameters are estimated using a version of EM algorithm accounting for the sampling design. The mean squared error estimation is also discussed. Two limited simulation studies are used to investigate the performance of the proposed method.This article is published as E. Gwak, J.K. Kim, and Y. Kim (2018). “A Random Effect Model Approach to Survey Data Integration," Statistics and Applications 16, 227-243. Posted with permission.</p

Pregnancy and Urban Environment (PRUNE) Cohort Profile and Built Environment in Infertile Couples

Background: Addressing the association between the perceived physical environment and human fertility is necessary to understand the impact of the built environment on reproductive health and develop effective interventions to improve human fertility. We assessed the association between perceived built environment and pregnancy in infertility patients. Methods: We constructed a prospective cohort study (Pregnancy and Urban Environment, PRUNE) recruiting 778 eligible infertility patients who visited one of the two university-affiliated infertility centers for infertility treatment between 2019 and 2022. Using a mobile survey, we collected the information of demographic, clinical characteristics, residential address, perceived proximity to neighborhood green and blue space, and environmental noise. Adjusted risk ratios (aRR) were calculated for the achievement of pregnancy within three months of survey participation. Results: In the 728 infertility patients, 445 completed the second round of survey. Median age of women and men was 39 and 40 years, respectively. Most reported they have green (91%) and blue space (67%) within a 10-min walking distance. A fourth of patients (26%) had an annoying environmental noise. Probability of pregnancy within three months was higher for those who had green space within walking distance (aRR = 1.18, 95% confidence interval: 1.06, 1.32). The association with pregnancy was close null for blue space and annoying environmental noise. The aRR for women and for men was comparable (p for interaction = 0.875). Conclusions: We observed a positive association between living close to green space and pregnancy. This finding would provide evidence of the potential impact of built environment on human fecundity in infertility couples. Clinical Trial Registration: This study is registered in the Clinical Research Information Service (https://cris.nih.go.kr, CRIS number: KCT0003560)

Does the father’s job matter? Parental occupation and preterm birth in Korea

OBJECTIVES Limited evidence is available regarding the impact of paternal occupation and its combined effect with maternal occupation on preterm birth. Therefore, we assessed the association of maternal and paternal occupations with preterm birth. METHODS We used the national birth data of Korea between 2010 and 2020. Parental occupations were divided into 5 categories: (1) managers; (2) professionals, technicians, and related workers; (3) clerks and support workers; (4) service and sales workers; and (5) manual workers. A multinomial logistic regression model was used to calculate the adjusted odds ratios (aORs) of extremely, very, and moderate-to-late preterm births per occupational category considering individual risk factors. RESULTS For the 4,004,976 singleton births, 40.2% of mothers and 95.5% of fathers were employed. Compared to non-employment, employment was associated with a lower risk of preterm birth. Among employed mothers, service and sales occupations were associated with a higher risk of preterm birth than managerial occupations (aOR, 1.06; 95% confidence interval [CI], 1.01 to 1.10 for moderate-to-late preterm births). The father’s manual occupation was associated with a higher risk of preterm birth (aOR, 1.09; 95% CI, 1.05 to 1.13 for moderate-to-late preterm) than managerial occupations. When both parents had high-risk occupations, the risk of preterm birth was higher than in cases where only the mother or neither of the parents had a high-risk occupation. CONCLUSIONS Paternal occupation was associated with preterm birth regardless of maternal employment and occupation and modified the effect of maternal occupation. Detailed occupational environment data are needed to identify the paternal exposures that increase the risk