Methodological Issues in Maternal Mortality Research

提案摘要 产妇死亡率是衡量保健质量的一个重要的公共卫生指标， 然而，美国还没有公布“官方”的美国孕产妇死亡率 2007年以来这造成了信息赤字，而国际社会的注意力更多地集中在 比以往任何时候都更加关注孕产妇死亡率。联合国千年发展目标 目标5a是在1990年至2015年期间将全球孕产妇死亡率降低75%。产妇死亡 在较新的联合国可持续发展目标中，减排也占有突出地位。最近， 在收集和编码美国孕产妇死亡率数据的主要问题已经确定， 目前对孕产妇死亡率和趋势的估计极不可靠。该研究分析了2015-16年 死因文字数据（在死亡证明的死因部分写的实际文字），以确定 并纠正孕产妇死亡数据收集和编码方面的问题。具体来说，我们将确定 记录不是孕产妇死亡，代表数据收集和编码中的错误， 根据世界卫生组织产妇死亡率定义，意外死亡原因。为 如果记录中没有明确的死亡原因，但被适当地归类为孕产妇死亡，我们将制定 补充编码方法，允许将死亡原因数据编码到特定器官系统， 疾病途径，从而提高报告的孕产妇死亡率数据的特异性。新开发 产妇死亡率估计数将与以前编码不准确的数据进行比较， 先前编码数据中的偏差程度。在项目的第二部分，我们将使用更准确的 以及目标1中编制的详细孕产妇死亡率数据，以分析按社会经济状况分列的孕产妇死亡率模式， 人口统计学和健康变量以及死亡原因。双变量和多变量方法将检查 这些变量与孕产妇死亡率之间的关系。拟议预算的主要贡献 研究包括：1）更好地了解孕产妇死亡率数据的质量; 2）制定更好的 分析孕产妇死亡率数据的方法; 3）提高数据质量的具体建议， 重新开始生产美国孕产妇死亡率; 4）更准确地估计美国孕产妇死亡率 按社会人口特征、死亡原因和区域分列的死亡率和差异。产生准确 孕产妇死亡率对于国家和国际报告以及监测 实现联合国可持续发展目标。识别特定的和潜在的 可预防的孕产妇死亡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的差异（例如， 产妇年龄、种族/民族或地区）将导致更准确地识别风险人群， 这对于有效地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.