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

总结和摘要 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严重差距 死亡率和严重孕产妇发病率（SMM），特别是可预防的死亡。 了解并解决这些死亡和 SMM 的根本原因对于黑人来说尤为紧迫， 原住民和其他出生的有色人种。黑人怀孕和产后的可能性是黑人的 3-4 倍 死于与怀孕相关的原因，并且与白人相比，SMM 的风险高出两倍 同行。种族不是一种生物结构，而是一种对健康产生真正影响的社会结构。两者都 结构性种族主义（这造成了不同种族的健康机会分配差异）以及 人际种族主义（显性和隐性偏见）对黑人、原住民和黑人的健康后果产生影响 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 产妇和妊娠结局，并且 BIPOC（黑人、原住民、有色人种）的预后更好 人口。目标 1 将链接来自两个大学医院系统的大型患者服务区域的数据 （密苏里大学和犹他大学）通过地理编码、社会保障死亡文件、讣告文件和 PCORnet 来 阐明健康的结构性和社会决定因素（SSDoH）对孕产妇死亡率和 SMM，并评估这些因素在多大程度上解释或预测这些结果的不平等 黑人生育的人。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 显着相关，有助于解释死亡率和 SMM 率的不平等 黑人和白人之间生育的人。目标 2 将询问来自以下机构的去识别化医疗记录： Cerner Corporation 的多机构 Real-World DataTM 系统可识别医院层面的相关因素 孕产妇死亡率和 SMM，并评估这些因素解释或预测不平等的程度 黑人生育者的这些结果。我们假设医院层面的因素，例如医疗 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和合并症 有重大影响。 拟议的研究具有创新性，因为它将 (a) 使用数据来开发和验证预后评分 孕产妇死亡率工具/SMM； (b) 评估可能导致孕产妇死亡率的医院层面因素， 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，以便更好地估计健康的结构性和社会决定因素（SSDoH）因素 孕产妇死亡率/SMM； (d) 鉴于其多地点性质，促进快速“扩大”到国家层面 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.