Impact of Childbearing Decisions on Family Size of Korean Women with Systemic Lupus Erythematosus.
In Je KimWook-Young BaekChang-Hee SuhYong Wook ParkHye Soon LeeSo-Young BangSang-Cheol BaeYoung Mo KangWon Kyung LeeHyesook ParkJi Soo LeePublished in: Journal of Korean medical science (2016)
Systemic lupus erythematosus (SLE) predominantly affects women in their reproductive years and has a significant impact on childbearing. We investigated the influence of personal decision on family size among Korean women with SLE and factors that affect the decisions. A case-control study comparing childbearing history and decisions of 112 SLE patients and 135 controls was performed. Women with SLE participating in the Network for Lupus Clinical Research in South Korea and matching controls between ages of 18-45, who are/were married or living with a partner were included. Data regarding socio-demographics, reproductive history, and childbearing decisions were collected through a survey using a standardized questionnaire and medical record review. More women with SLE reported at least one pregnancy (85.7% vs. 71.9%, P = 0.009) or at least one live birth (85.7% vs. 71.9%, P = 0.003) compared with controls. Mean number of pregnancies was significantly higher (2.4 ± 1.6 vs. 1.4 ± 1.3, P < 0.001), and mean number of live births was significantly lower in women with SLE (1.2 ± 0.8 vs. 1.6 ± 0.8, P < 0.001). Significantly more women with SLE made the decision not to have children compared with controls (54.5% vs. 40.7%, P = 0.031), and health-related concerns were the major cause of the decision. Other socio-demographic factors did not influence the decision to limit childbearing in SLE women. The disease-related concerns had significant impact on family size and childbearing decisions among Korean women with SLE.
Keyphrases
- systemic lupus erythematosus
- disease activity
- pregnancy outcomes
- decision making
- end stage renal disease
- healthcare
- chronic kidney disease
- preterm birth
- polycystic ovary syndrome
- ejection fraction
- type diabetes
- pregnant women
- metabolic syndrome
- insulin resistance
- skeletal muscle
- deep learning
- electronic health record
- patient reported outcomes
- african american
- breast cancer risk
- network analysis
- high speed