Methodological Issues in Maternal Mortality Research

建议书摘要 孕产妇死亡率是衡量卫生保健质量的关键公共卫生指标 国内和国际上；然而，美国还没有公布美国产妇死亡率的“官方”数据 自2007年以来。这造成了信息赤字，而与此同时，更多的国际关注 比以往任何时候都更加关注产妇死亡率。例如，联合国千年发展计划 目标5a是在1990年至2015年期间在全球范围内将孕产妇死亡率降低75%。孕产妇死亡率 减少在新的联合国可持续发展目标中也占有重要地位。最近， 已经确定了美国孕产妇死亡率数据收集和编码中的主要问题，这些问题使得 目前对孕产妇死亡水平和趋势的估计非常不可靠。这项研究分析了2015-16年 死因文字数据(写在死亡证明死因部分的实际文字)，以识别 纠正孕产妇死亡数据收集和编码方面的问题。具体地说，我们将确定 记录不是孕产妇死亡，而是正确排除数据收集和编码中的错误 根据世界卫生组织孕产妇死亡定义，意外死亡原因。为 有非特定死因的适当归类为孕产妇死亡的记录，我们将制定 补充编码方法，允许将死因数据编码到特定器官系统和 疾病途径，从而增加了报告的孕产妇死亡数据的特异性。新开发的 孕产妇死亡率估计值将与之前不准确编码的数据进行比较，以评估 先前编码的数据中的偏差程度。在项目的第二部分，我们将使用更准确的 在目标1中制定了详细的孕产妇死亡率数据，以分析按社会和性别分列的孕产妇死亡率模式 人口统计和健康变量以及死因。双变量和多变量方法将检验 这些变量与产妇死亡率之间的关系。建议的主要贡献 研究包括：1)更好地了解孕产妇死亡数据的质量；2)开发改进的 分析孕产妇死亡数据的方法；3)改进数据质量所需的具体建议 重新开始生产美国孕产妇死亡率；以及4)更准确地估计美国孕产妇死亡率 按社会人口学特征、死因和地区划分的死亡率和差异。生产精准 孕产妇死亡率对于国家和国际报告以及监测在以下方面取得的进展至关重要 实现联合国可持续发展目标。识别特定的和潜在的 孕产妇死亡的可预防原因将导致有效地将预防工作的目标对准最 有问题的死因。更详细地了解孕产妇死亡率差距(例如，通过 产妇年龄、种族/民族或地区)将导致更准确地识别高危人群， 对有效确定预防方案的目标至关重要。

Proposal Summary Maternal mortality is a sentinel public health indicator essential to the measurement of health care quality both nationally and internationally; yet, the United States has not published an “official” U.S. maternal mortality rate since 2007. This has created an information deficit at a time when more international attention has been focused on maternal mortality than ever before. For example, the United Nations Millennium Development Goal 5a was to reduce maternal mortality by 75% worldwide between 1990 and 2015. Maternal mortality reduction also figures prominently in the newer United Nations Sustainable Development Goals. Recently, major problems in the collection and coding of U.S. maternal mortality data have been identified, which make current estimates of maternal mortality levels and trends highly unreliable. This study analyzes the 2015-16 cause of death literal data (actual words written in the cause-of-death section of the death certificate) to identify and correct problems in data collection and coding of maternal deaths. Specifically, we will determine which records are not maternal deaths and represent errors in data collection and coding by correctly excluding incidental causes of death according to the World Health Organization maternal mortality definition. For records with non-specific causes of death that are appropriately classified as maternal deaths, we will develop supplementary coding methods which allow the cause of death data to be coded to specific organ systems and disease pathways, thus increasing the specificity of reported maternal mortality data. Newly developed maternal mortality estimates will be compared to those from the previous inaccurately coded data to assess the degree of bias in the previously coded data. In the second part of the project, we will use the more accurate and detailed maternal mortality data developed in Aim 1 to analyze maternal mortality patterns by socio- demographic and health variables and cause of death. Bivariate and multivariate methods will examine associations between these variables and the maternal mortality rate. Major contributions of the proposed research include: 1) a better understanding of maternal mortality data quality; 2) development of improved methods for analyzing maternal mortality data; 3) concrete recommendations to improve data quality needed to restart production of U.S. maternal mortality rates; and 4) more accurate estimation of U.S. maternal mortality rates and disparities by socio-demographic characteristics, cause of death, and region. Producing accurate maternal mortality rates is essential for national and international reporting, and for monitoring progress toward meeting the United Nation’s Sustainable Development Goals. The identification of specific and potentially preventable causes of maternal death will lead to efficient targeting of prevention efforts towards the most problematic causes of death. More detailed knowledge of maternal mortality disparities (for example, by maternal age, race/ethnicity, or region) will lead to the more accurate identification of at-risk populations, essential to effective targeting of prevention programs.