PROXIMATE DETERMINANTS OF LBW AND INFANT MORTALITY

降低低出生体重(LBW)和婴儿死亡率(IM) 美国公共卫生服务的突出目标。 拟议的研究将有助于提供新的信息轮廓 LBW和IM的参数和原因。这项研究是 在莫斯利-陈分析框架的指导下。它的重点是 通过以下方法确定LBW和IM的近似决定因素 产妇年龄以及她们的种族、民族和经济状况 相关的生物社会和生物行为根源。数据来自 1976-1980年全国健康和营养检查调查及 1982-1984年西班牙裔健康和营养检查调查 将被分析以估计吸烟和其他疾病的流行率 不良妊娠结局的特定生物行为危险因素 在育龄妇女中。这些流行率将是 根据年龄、平价、种族、西班牙裔种族和 社会经济因素。吸烟和吸烟的几率 特定的疾病将被建模为社会和 使用对数线性建模技术的人口统计因素。这个 生殖健康状况恶化的假设 黑人、白人和西班牙裔妇女的不同比率将是 测试，以及生育模式(产妇年龄、出生)的影响 间距、奇偶性)对这些劣化率进行了评估。 按产次和产妇年龄划分的LBW和IM比率将为 估计为种族、西班牙裔民族和社会经济群体 使用关联的出生和婴儿死亡证明数据磁带 1985年加利福尼亚州、纽约州、南卡罗来纳州和密苏里州出生 一群人。将LBW和IM的赔率建模为以下函数 产妇的年龄，种族，民族，社会经济因素， 产前护理、医疗条件、吸烟和获得 新生儿重症监护技术，并估计其相对 LBW和IM在层内和层间的风险，对数线性建模 这些技术也将应用于这些数据。 在莫斯利-陈模型下，拟议的研究将 除了对高危人群进行人口统计识别外， 超越生物医学对LBW和IM病因学的关注 通过个人层面上的疾病过程。更确切地说，疾病 进程将与人口连接。因果建模将 用于定位疾病的社会和行为相关因素 并确定其对出生体重的影响 人口中的分布和婴儿死亡率 大号的。

Reducing low birth weight (LBW) and infant mortality (IM) are prominent objectives of the United States Public Health Service. The proposed research will contribute new information outlining the parameters and causes of LBW and IM. This research is guided by the Mosley-Chen analytic framework. It focuses on identifying the proximate determinants of LBW and IM by maternal age and their racially, ethnically, and economically associated biosocial and biobehavioral roots. Data from the National Health and Nutrition Examination Survey, 1976-1980 and the Hispanic Health and Nutrition Examination Survey, 1982-1984 will be analyzed to estimate prevalences of smoking and other specific biobehavioral risk factors for poor pregnancy outcomes among women of childbearing age. These prevalence rates will be estimated by age, parity, race, Hispanic ethnicity, and socioeconomic factors. The odds of smoking and of having specific diseases will be modeled as functions of social and demographic factors using log-linear modeling techniques. The hypothesis that reproductive health status deteriorates at differential rates for black, white, and Hispanic women will be tested, and the influence of fertility patterns (maternal age, birth spacing, parity) on these deterioration rates assessed. Rates of LBW and IM by parity and maternal age will be estimated for racial, Hispanic ethnic, and socio-economic groups using linked birth and infant death certificate data tapes for the 1985 California, New York, South Carolina, and Missouri birth cohorts. To model the odds of LBW and IM as functions of maternal age, race, ethnicity, socioeconomic factors, parity, prenatal care, medical conditions, smoking, and access to neonatal intensive care technology, and to estimate the relative risk of LBW and IM within and between strata, log-linear modeling techniques will also be applied to these data. Under the Mosley-Chen model, the proposed research will go beyond the demographic identification of high risk populations, and beyond the biomedical focus on the etiology of LBW and IM through disease processes on the individual level. Rather, disease processes will be connected to populations. Causal modeling will be used to locate the social and behavioral correlates of disease processes and identify their consequences for birthweight distributions and infant mortality rates among populations at large.