Factors Associated with Pregnant Women for Choosing Deliver at Clinics or Hospital - Penghu Area as Example

• Main Authors: Huang-Min Yu, 黃敏毓
• Other Authors: Yong-yuan Chang
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/82956293724276236745

碩士 === 高雄醫學大學 === 公共衛生學研究所碩士在職專班 === 93 === Factors Associated with Pregnant Women for Choosing Deliver at Clinics or Hospital ------ Penghu Area as Example Abstract Objectives： According to the statistics of Health Department of Executive Yuan,after health care insurance carry out,that pregnant women choose hospital to deliver more often than clinics in recent years. The main reason is that people choose medical organizations more convenient than before under health care insurance. Pregnant women are more likely to receive prenatal exam and deliver at hospital due to good medical equipments. This condition changes the delivery rate at clinics and hospitals in Taiwan,but this situation is not same as Penghu.The Penghu County Health Bureau reported that Penghu birth rate at hospital and clinics was forty eight over fifty two last year. What’s the factors associated with pregnant women for choosing delivery place in Penghu.The purposes of this study including： To know pregnant women’s medical behavior and demographic, enabling factors in Penghu area. To know pregnant women’s medical need and environmental factors in Penghu area. Set up a logistic regression model for explaining pregnant women’s delivery place. Methods： This study use Andersen model to design structured questionnaires for analyzing the pregnant women’s medical behavior model that choose delivery place . This study uses multiple logistic regression analysis with SPSS for Windows 10.0,trying to find out the factors associated with pregnant women for choosing delivery place. This target population are pregnant over 28 weeks (third trimester) women in Penghu area. Pregnant women in Penghu conform the above requirement receive questionnaires at hospital or clinics that have delivery service. Survey period was between Jan. 15 and March 15,2005. Results: Pregnant women’s age, education, occupation, religion, family income per month, private insurance, payment items by patient, waiting time, obstetric complication, previous bad delivery experience, fix doctor deliver, delivery is nature procedure concept and so on are significant relation in pregnant women choose delivery place. Put these 12 factors into multiple logistic regression analysis model. We find that education, private insurance, delivery is nature procedure concept, previous bad delivery experience, fix doctor deliver and so on have significant differences at clinics and hospital. Synthesize statistic find that the first three thinking factors in penghu are doctor, medical equipments, and ward installation. The last three thinking factors are traffic, economic, and waiting time. Discussion: This research reveal that pregnant women choose hospital to deliver have old age, high socioeconomic level and not consider delivery is nature procedure. Hospital must propagate to young age and low socioeconomic pregnant women with that “thought delivery is nature procedure, but not everyone is so smooth. Good emergency equipments is the only method to ensure delivery safe”. Pregnant women choose clinics to deliver hope fix doctor. Hospital’s obstetric doctor come from Taiwan and usual leave penghu at day off make patient not feel safty. This study suggest that penghu’s hospital have to train locality young obstetric doctor and voluntary to stay in hospital for service. Long waiting time is the cause of pregnant women choose clinics to deliver. Hospital must simpling work procedure or increase frequency of outpatient service for shorten waiting time. Too much payment by patient and previous bad delivery experience usual change delivery place in clinics patient. If clinics want to attract pregnant women to deliver must decrease payment items and elevate service quality. Key words: Penghu area, pregnant women, delivery place, multiple logistic regression analysis