DRIVERs: Data systems Research to Identify driVers of Ethnic & Racial Inequities in Maternal Mortality

内容和摘要 在理解导致孕产妇死亡的医院和结构层面的原因方面存在重大差距。 在孕产妇死亡率和孕产妇严重发病率方面，特别是在可预防的死亡方面。 了解和解决这些死亡率和SMM的根本原因对黑人来说尤为紧迫， 原住民和其他有色人种。黑人怀孕和产后的人是3 - 4倍 死于妊娠相关原因，患SMM的风险是白色女性的两倍 同行种族不是一种生物构造，而是一种具有真实的健康后果的社会构造。两 结构性种族主义（造成按种族不同的健康机会分配）， 人与人之间的种族主义（显性和隐性偏见）在黑人、土著人和 其他生育的人。 我们的中心假设是，健康的社会决定因素和医院因素显着影响 孕产妇和妊娠结局，并且对BIPOC（黑人，土著人，有色人种）更具预后意义 人口数量。目标1将连接来自两个大学医院系统的大型患者服务区域的数据 （密苏里州和犹他州的大学）与地理编码，社会安全死亡文件，讣告文件，和PCORnet， 阐明健康的结构和社会决定因素对孕产妇死亡率的影响， SMM，并评估这些因素在多大程度上解释或预测这些结果中的不公平， 黑人生育我们假设结构性种族主义的下游后果将产生 与死亡率和SMM显著相关，并将有助于解释死亡率和SMM率的不平等 黑人和白色人之间的区别Aim 2将询问来自 Cerner Corporation的多机构Real-World DataTM系统，用于识别医院级相关因素 与孕产妇死亡率和SMM之间的关系，并评估这些因素在多大程度上解释或预测了 这些结果在黑人生育人群中。我们假设，医院层面的因素，如医疗 服务隔离、孕产妇护理水平、城市/农村状况以及患者人口统计学和合并症 有重大影响。 拟议的研究是创新的，因为它将（a）使用数据来开发和验证预后评分 产妇死亡率/SMM工具;（B）评估医院一级可能导致产妇死亡的因素， 使用Cerner数据库中128个独立卫生系统的数据;（c）整合地理编码和关联死亡 数据有助于更好地估计健康的结构性和社会决定因素（SSDoH） （d）考虑到产妇死亡率/SMM的多地点性质， 大平原协作（GPC）/PCORnet数据系统。

Summary and Abstract There is a critical gap in understanding hospital- and structural-level causes leading to maternal mortality and severe maternal morbidity (SMM), particularly with regard to preventable deaths. Understanding and addressing these root causes of mortality and SMM is particularly urgent for Black, Indigenous and other birthing people of color. Black pregnant and postpartum people are 3-4 times more likely to die from pregnancy-related causes and have a two-fold higher risk of SMM compared to their White counterparts. Race is not a biological construct, but a social one with real health consequences. Both structual racism (which creates differential distribution of opportunities for health by race), and experiences of interpersonal racism (explicit and implicit bias) play a role in health consequences for Black, Indigenous, and other birthing people. Our central hypothesis is that social determinants of health and hospital factors significantly impact maternal and pregnancy outcomes, and are more prognostic for BIPOC (Black, Indigenous, People of Color) populations. Aim 1 will link data from the large patient catchment areas of two university hospital systems (Universities of Missouri and Utah) with geocoding, social security death files, obituary files, and PCORnet to elucidate the impact of structural and social determinants of health (SSDoH), on rates of maternal mortality and SMM, and to assess the extent to which these factors explain or predict inequity in these outcomes among Black birthing people. We hypothesize that downstream consequences of structural racism will have an significant association with mortality and SMM, and will help explain inequity in mortality and SMM rates between Black and White birthing people. Aim 2 will interrogate de-identified healthcare records from the Cerner Corporation's multi-institutional Real-World DataTM system to identify hospital-level factors associated with maternal mortality and SMM, and to assess the extent to which these factors explain or predict inequity in these outcomes among Black birthing people. We hypothesize that hospital-level factors such as medical services segregation, maternal levels of care, urban/rural status, and patient demographics and comorbidities have significant impact. The proposed research is innovative, as it will (a) use data to develop and validate a prognostic scoring tool for maternal mortality/SMM; (b) assess hospital-level factors that may contribute to maternal mortality, using data from 128 separate health systems in Cerner's database; (c) integrate geocoding and linked death data to enable better estimates of structural and social determinants of health (SSDoH) factors that contribute to maternal mortality/SMM; and (d) facilitate a rapid “scale-up” to a national level, given the multi-site nature of the Greater Plains Collaborative (GPC)/PCORnet data system.