臺灣博碩士論文加值系統

　　本研究的目的是分析孕婦懷孕週數大於24週後胎兒體重變化的影響因素、預測新生兒出生體重、及繪製孕婦懷孕24週後胎兒體重分佈的百分位數預測圖。在統計方法上，本研究使用線性迴歸模式測量誤差校正公式修正自變項測量誤差所造成的估計偏誤與廣義工具變項估計法修正內生性自變項所造成的估計偏誤。茲將本研究的結論分述如下：
一、影響胎兒體重變化的因子：
　　配適隨機效果模型時，先估計每位胎兒在懷孕24週後胎兒體重隨時間變化之簡單線性迴歸模型的截距項與斜率，並進而發現會隨著時間影響胎兒體重的因子包含產檢時間、前胎剖腹生產、懷孕年齡≤ 24歲、孕前體重、孕期增加體重、30 ≤ BMI < 35 (孕前中度肥胖)、懷孕引起高血壓、妊娠糖尿病，與子癲症等變項。
二、預測新生兒出生體重的因子：
　　修正自變項測量誤差問題後，影響新生兒出生體重的因子包含胎兒體重成長截距項、胎兒體重成長斜率、過去生產活產數、男嬰、孕前身高、孕前體重、孕期增加體重、懷孕週數、懷孕週數≥ 37週、24 ≤ BMI < 27 (孕前過重)、胎兒窘迫，與氣喘病等變項。
　　進一步修正內生性自變項問題後，影響新生兒出生體重的因子包含胎兒體重成長速度異常(胎兒體重斜率≤150公克)、過去生產活產數、男嬰、孕前體重、孕期增加體重、孕期增加體重5公斤、懷孕週數、30 ≤ BMI < 35(孕前中度肥胖)、妊娠糖尿病，與子癲症等變項。
三、繪製懷孕24週後胎兒體重分佈的百分位數預測圖：
　　分別配適分量迴歸模型求取第5、25、50、75，與第95個百分位數的胎兒體重預測值來繪圖。孕婦在產檢時間38週後，胎兒體重成長速度漸趨平緩，檢視胎兒體重預測值大於3500公克時是第95百分位，小於3000公克則是低於第50百分位。

The objectives of this study were to analyze the effects of predictive factors on the marginal mean of time-varying fetal weight after 24 week pregnancy, to predict the mean of newborn’s birth weight, and to plot the predicted quantiles of time-varying fetal weight after 24 week pregnancy. The statistical methodologies applied in this study included the correction of the estimation bias due to the measurement errors of some covariates in linear regression model and the application of the generalized instrumental variables (GIV) estimation method to adjust the estimation bias caused by the endogeneity of some covariates. The main findings are listed below:
1. The predictive factors that affected the marginal mean of time-varying fetal weight after 24 week pregnancy:
　　When fitting random effects model, we obtained the parameter estimated intercept and slope of a simple linear regression model for modeling the mean of each baby’s fetal weight by time after 24 week pregnancy, and then found that the predictive factors affecting the marginal mean of time-varying fetal weight after 24 week pregnancy included the examining times, prior history of cesarean section, maternal age ≤ 24 years, pre-pregnancy weight, pregnancy weight gain, 30 ≤ pre-pregnancy BMI < 35 (moderate obesity), pregnancy-induced hypertension, gestational diabetes mellitus, and eclampsia.
2. The predictive factors that affected the mean of newborn’s birth weight:
　　After correcting the estimation bias due to measurement errors, the predictive factors that affected the mean of newborn’s birth weight included the fetal weight’s intercept, fetal weight’s slope, male infant, parity (number of live born infants), mother’s height, mother’s pre-pregnancy weight, mother’s pregnancy weight gain, gestation weeks, gestation ≥37 weeks, 24 ≤ pre-pregnancy BMI < 27(overweight), fetal distress, and asthma.
　　After further adjusting for the estimation bias caused by the endogenous cova-riates, the predictive factors that affected the mean of newborn’s birth weight in-cluded the abnormality of fetal weight’s slope (i.e., fetal weight’s slope ≤ 150g/week), male infant, parity (number of live born infants), mother’s height, mother’s pre-pregnancy weight, mother’s pregnancy weight gain, mother’s weight gain ≤ 5 kg, gestation weeks, 30 ≤ pre-pregnancy BMI< 35 (moderate obesity), gestational diabetes mellitus, and eclampsia.
3. Plot of the predicted quantiles of time-varying fetal weight after 24 week pregnancy:
　　After plotting the predicted 5th, 25th, 50th, 75th, and 95th percentiles of the fetal weight over 24 weeks obtained from the fitted quantile regression models, we found that the pregnant woman after 38 gestation weeks, the fetal weight growth speed gradually slow down gently, at which the predicted 50th and 95th percentiles of the fetal weight were 3000 and 3500 grams respectively.

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圖 次-------------------------------------------------------------ii
第壹章 緒論 3
第一節 研究背景 3
第二節 研究動機 5
第三節 研究目的 9
第四節 研究流程 10
第貳章 文獻探討 11
第一節 胎兒體重估計之定義 11
第二節 孕婦健康狀況對新生兒出生體重的影響 14
第參章 研究方法 17
第一節 研究對象與範圍 17
第二節 理論架構 19
第三節 研究設計 20
第四節 變項說明 23
第五節 統計分析 27
第六節 分析方法 30
第肆章 結果 46
第一節 描述性分析 46
第二節 影響胎兒體重變化之因素 53
第三節 預測新生兒出生體重 57
第伍章 討論與建議 70
參考文獻 73
一、中文部份 73
二、英文部份 75
附錄：SAS程式指令 77

一、中文部份
王宗曦 (民94)，「從產前檢查談傳染病防治」，健康世界雜誌，2月號。
李珮珍，郭素珍，鄧森文，呂宗學，李中一 (民92)，「台灣地區嬰兒出生體重與妊娠週數分佈之長期變化趨勢」，台灣公共衛生雜誌，22卷5期，頁376-385。
季瑋珠，賴佳君 (民90)，「全民健保預防保健服務使用率之分析」，台灣公共衛生雜誌，20卷1期，2001年2月，頁43-51。
林勤豐，黃月桂 (民89)，「成人健康檢查保健評估與最佳產品組合之探討」，醫院，第33卷第2期，頁60-70。
林欣蓉，李美玲 (民91)，「嬰兒健康生產函數：台灣的實證研究」，國立台灣大學經濟系出版，經濟論文叢刊，第30輯，第1 期，3月。
朱益民(民97)，「淺談妊娠高血壓」，從http://www.kgh.com.tw/health/04-210.html (97/05/03)。
孫彤，卞度宏，吳味辛，張曲，何小明 (民88)，「根據胎兒體積模型超聲預測胎兒體重」，中華超聲影像學雜誌，第8卷第3期。
桂芳(民95)，「應用改良公式估計胎兒體重」，護理學雜誌， 21卷(24期) 。
陳泰安(民94)，「台灣地區早產與低出生體重嬰兒死亡趨勢分析」，高雄醫學大學公共衛生學研究所碩士論文。
陳培君，李中一，林瑞雄，宋鴻樟(民91)，「婦女抽菸及二手煙暴露和低出生體重嬰兒」，台灣公共衛生雜誌，21卷5期，頁321-328。
內政部統計處 (民97)，「內政統計通報」，從http://www.moi.gov.tw/stat/ (97/06/30)。
楊淑婷 (民95)，「婦女孕期工作狀況與低體重新生兒之相關性探討」，私立長榮大學職業安全與衛生研究所碩士論文。
劉乃維 (民94)，「妊娠婦女產前檢查對新生兒健康的影響」，國立中央大學產業經濟研究所碩士論文。
賴宗炫 (民97)，「肚子裡的寶寶體重標準嗎？」賴宗炫醫師個人網站，從http://www.joseph-lai.idv.tw/CONTENTS/main3_6_b1.html (97/01/22)。
謝昌宏 (民96)，「婦女在懷孕時之飲食習慣及行為調查」，私立中山醫學大學營養學研究所碩士論文。
陳麗美、謝邦昌，高森永(民93)，台灣地區0至6歲兒童體位標準之建立: 行政院衛生署國民健康局。
台灣諾和諾德藥品股份有限公司 (民97) ，從http://www.novonordisk.com.tw/documents/article_page/document/GrowthChart.asp (97/07/01)。 
二、英文部份
Bailey, J., D., Alton, and M, S. (2005). Routine Investigations Might be Useful in Preeclampsia, But Not in Gestational Hypertesion. Aust N Z J Obstet Gynaecol, 45, pp. 144-147.
Benacerraf, B. R., R, G., and Frigoletto, F. J. (1988). Sonogra phically pa-rameter estimated fetal weights: accuracy and limitation. Am J Obstet Gynecol, 159(5), pp. 1118-1121.
Carroll, R. J., Ruppert, D., Stefanski, L. A., and Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models：A Modern Perspective (second ed.): Taylor and Francis Group, LLC.
Chen, X. K., Wen, S. W., Smith, G., Yang, Q., and Walker, M. (2006). Pregnancy-induced hypertension is associated with lower infant mortality in preterm singletons. BJOG: An International Journal of Obstetrics and Gynaecology, 113(5), pp. 544-551.
Dakwa, K., and Das, G. C. (2002). Estimation of fetal birth weight using ultrasonographic parameters. J Nepal Medical Association, 39, pp. 284-286.
Greene, W. H. (2000). Econometric Analysis (FIFTH EDITION ed.). New Jersey, 07458.: Pearson Education, Inc.,Upper Saddle River,.
Kaminski, M., Goujard, J., and Rumeau-Rouquette, C. (1973). Prediction of low birth weight and prematurity by a multiple regression analysis with maternal characteristics known since the beginning of the preg-nancy. Epidemiol, 2, pp. 195-204.
Koenker, R., and Hallock, K. F. (2001). Quantile Regression. Journal of Economic Perspectives, 15, pp. 143-156.
Mary L Hediger, T. S., Isadore G Ances, Daniel H Be/sky, and Ruth Wex-berg Salmon. (1990). Rate and amount of weight gain during ado-lescent pregnancy: associations with maternal weight-for-height and birth weight13. American Society for Clinical Nutrition, 52, pp. 793-799.
May, R. (2007). Prepregnancy Weight, Inappropriate Gestational Weight Gain, and Smoking: Relationships to Birth Weight. AMERICAN JOURNAL OF HUMAN BIOLOGY, 19, pp. 305-310.
Mongelli, M., and Gardosi, J. (1995). Longitudinal study of fetal growth in subgroups of a low-risk population. Ultrasound in Obstetrics and Gynaecology, 6(5), pp. 340-344.
Shau, W.-Y., Tsai, T.-L., and Hu, F.-C. (2006). Generalized path analysis and generalized simultaneous equations model for analyzing inter-mediate variables. Unpublished manuscript.
Sherman, D. J., Arieli, S., Tovbin, J., Siegel, G., Caspi, E., and Bukovsky, I. (1998). A comparison of clinical and ultrasonic estimation of fetal weight. Obstet Gynecol, 91(2), pp. 212-217.
Shrestha, D. M. (2004). Sonographic Estimation of Fetal Weight: Evalua-tion of Five Formulas. Unpublished M.B.B.S, Tribhuvan University, Kathmandu, Nepal.
Stein, A., and Susser, M. (1984). Intrauterine growth retardation: epidemi-ological issues and public health significance. semin Perinatol, 8, pp. 5-14.
Tough, S., Svenson, L., and Johnston, D. (2001). Characteristics of preterm delivery and low birth weight among 113,994 infants in Alberta: 1994-1996. Can J Public Health, 92 (4), pp. 276-280.
Tsai, T.-L., Shau, W.-Y., and Hu, F.-C. (2006). Generalized Path Analysis and Generalized Simultaneous Equations Model for Recursive Sys-tems with Responses of Mixed Types. Structural Equation Modeling: A Multidisciplinary Journal, 13(2), pp. 229-251.
Walker, M. B., and Wallace, S. (1998). The implications of current policies on the production of infant health. Applied Economics, 30(9), pp. 1177 - 1186.