Neighborhood risk factors for low birthweight in Baltimore: A multilevel analysis
Neighborhood risk factors for low birthweight in Baltimore: A multilevel analysis
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DOI：
10.2105/ajph.87.7.1113
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发表时间：
1997-07-01
影响因子：
12.7
通讯作者：
Caughy, MO
中科院分区：
文献类型：
--
作者：
OCampo, P;Xue, XN;Caughy, MO
目标。过去对低出生体重的研究主要集中在个人层面的风险因素上。我们试图通过使用人口普查区层面的社会分层、社区赋权和环境压力因素数据来评估宏观社会因素的贡献。1985年至1989年期间，人口普查区层面的社会风险信息与马里兰州巴尔的摩市的证书记录相关联，个人层面的因素包括产妇教育程度、产妇年龄、医疗援助健康保险（Medicaid）和产前护理开始的三个月。采用两阶段回归分析的多层次建模方法。宏观因素与低出生体重有直接联系和相互作用。所有个体危险因素均与宏观变量存在交互作用；也就是说，低出生体重的个体风险因素表现不同，取决于居住社区的特征。例如，生活在高风险社区的妇女从产前护理中受益的程度低于生活在低风险社区的妇女。多层次建模是一种重要的工具，可以同时研究宏观和个人层面的风险因素。多层次分析应在公共卫生政策的制定中发挥更大的作用。
Objectives. Past research on low birthweight has focused on individual-level risk factors. We sought to assess the contribution of macrolevel social factors by using census tract-level data on social stratification, community empowerment, and environmental stressors.Methods. Census tract-level information on social risk was linked to blah certificate records from Baltimore, Md, for the period 1985 through 1989, Individual-level factors included maternal education, maternal age, medical assistance health insurance (Medicaid), and trimester of prenatal care initiation. Methods of multilevel modeling using two-stage regression analyses were employed.Results. Macrolevel factors had both direct associations and interactions with low birthweight. All individual risk factors showed interaction with macroleveI variables; that is, individual-level risk factors for low birthweight behaved differently depending upon the characteristics of the neighborhood of residence, For example, women living in high-risk neighborhoods benefited less from prenatal care than did women living in lower-risk neighborhoods.Conclusions. Multilevel modeling is an important tool that allows simultaneous study of macro- and individual-level risk factors. Multilevel analyses should play a larger role in the formulation of public health policies.