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

摘要和摘要 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，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Black来说尤为紧迫， 土著和其他出生的有色人种。黑人孕妇和产后的几率是其他人的3-4倍 死于妊娠相关原因，罹患SMM的风险是白人的两倍 对口单位。种族不是一种生物结构，而是一种具有真正健康后果的社会结构。两者都有 结构性种族主义(按种族造成健康机会的不同分配)，以及 人际种族主义(显性和隐性偏见)在黑人、土著和 其他分娩的人。 我们的中心假设是，健康的社会决定因素和医院因素显著影响 产妇和妊娠结局，对BIPOC(黑人、土著、有色人种)的预后更具预测性 人口。目标1将连接来自两个大学医院系统的大型患者集散区的数据 (密苏里州和犹他州大学)使用地理编码、社会保障死亡文件、讣告文件和PCORnet来 阐明结构和社会健康决定因素(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中的不公平 黑人分娩的人。我们假设结构性种族主义的下游后果将有一个 与死亡率和SMM显著相关，并将有助于解释死亡率和SMM率之间的不平等 在黑人和白人出生的人之间。AIM 2将询问来自 Cerner公司的多机构Real-World DataTM系统用于识别与医院级别相关的因素 与孕产妇死亡率和SMM之间的关系，并评估这些因素解释或预测在 这些结果是在黑人生育人群中产生的。我们假设医院层面的因素，如医疗保健 服务隔离、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的工具；(B)评估可能导致产妇死亡率的医院一级因素， 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，以便更好地估计有助于健康的结构性和社会决定因素(SSDoH)因素 (D)促进在国家一级迅速“扩大”，考虑到艾滋病毒/艾滋病的多地点性质 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.