PDF(Pubmed)
Abstract：
Hysterectomy is a common surgery among reproductive-aged U.S. patients, with rates highest among Black patients in the South. There is limited insight on causes of these racial differences. In the U.S., electronic medical records (EMR) data can offer richer detail on factors driving surgical decision-making among reproductive-aged populations than insurance claims-based data. Our objective in this cohort profile paper is to describe the Carolina Hysterectomy Cohort (CHC), a large EMR-based case-series of premenopausal hysterectomy patients in the U.S. South, supplemented with census and surgeon licensing data. To demonstrate one strength of the data, we evaluate whether patient and surgeon characteristics differ by insurance payor type.
We used structured and abstracted EMR data to identify and characterize patients aged 18-44 years who received hysterectomies for non-cancerous conditions between 10/02/2014-12/31/2017 in a large health care system comprised of 10 hospitals in North Carolina. We used Chi-squared and Kruskal Wallis tests to compare whether patients\' socio-demographic and relevant clinical characteristics, and surgeon characteristics differed by patient insurance payor (public, private, uninsured).
Of 1857 patients (including 55% non-Hispanic White, 30% non-Hispanic Black, 9% Hispanic), 75% were privately-insured, 17% were publicly-insured, and 7% were uninsured. Menorrhagia was more prevalent among the publicly-insured (74% vs 68% overall). Fibroids were more prevalent among the privately-insured (62%) and the uninsured (68%). Most privately insured patients were treated at non-academic hospitals (65%) whereas most publicly insured and uninsured patients were treated at academic centers (66 and 86%, respectively). Publicly insured and uninsured patients had higher median bleeding (public: 7.0, uninsured: 9.0, private: 5.0) and pain (public: 6.0, uninsured: 6.0, private: 3.0) symptom scores than the privately insured. There were no statistical differences in surgeon characteristics by payor groups.
This novel study design, a large EMR-based case series of hysterectomies linked to physician licensing data and manually abstracted data from unstructured clinical notes, enabled identification and characterization of a diverse reproductive-aged patient population more comprehensively than claims data would allow. In subsequent phases of this research, the CHC will leverage these rich clinical data to investigate multilevel drivers of hysterectomy in an ethnoracially, economically, and clinically diverse series of hysterectomy patients.
We used structured and abstracted EMR data to identify and characterize patients aged 18-44 years who received hysterectomies for non-cancerous conditions between 10/02/2014-12/31/2017 in a large health care system comprised of 10 hospitals in North Carolina. We used Chi-squared and Kruskal Wallis tests to compare whether patients\' socio-demographic and relevant clinical characteristics, and surgeon characteristics differed by patient insurance payor (public, private, uninsured).
Of 1857 patients (including 55% non-Hispanic White, 30% non-Hispanic Black, 9% Hispanic), 75% were privately-insured, 17% were publicly-insured, and 7% were uninsured. Menorrhagia was more prevalent among the publicly-insured (74% vs 68% overall). Fibroids were more prevalent among the privately-insured (62%) and the uninsured (68%). Most privately insured patients were treated at non-academic hospitals (65%) whereas most publicly insured and uninsured patients were treated at academic centers (66 and 86%, respectively). Publicly insured and uninsured patients had higher median bleeding (public: 7.0, uninsured: 9.0, private: 5.0) and pain (public: 6.0, uninsured: 6.0, private: 3.0) symptom scores than the privately insured. There were no statistical differences in surgeon characteristics by payor groups.
This novel study design, a large EMR-based case series of hysterectomies linked to physician licensing data and manually abstracted data from unstructured clinical notes, enabled identification and characterization of a diverse reproductive-aged patient population more comprehensively than claims data would allow. In subsequent phases of this research, the CHC will leverage these rich clinical data to investigate multilevel drivers of hysterectomy in an ethnoracially, economically, and clinically diverse series of hysterectomy patients.
摘要：
背景：子宫切除术是美国育龄期患者的常见手术，南方黑人患者的发病率最高。对这些种族差异的原因了解有限。在美国,与基于保险索赔的数据相比,电子病历(EMR)数据可以为生育年龄人群提供更丰富的驱动手术决策的因素细节.我们在这篇队列简介论文中的目标是描述卡罗莱纳州子宫切除术队列(CHC)，一个基于EMR的大型病例系列，在美国南部的绝经前子宫切除术患者，补充了人口普查和外科医生许可数据。为了证明数据的一种优势，我们评估患者和外科医生的特征是否因保险付款人类型而异.
方法：我们使用结构化和抽象的EMR数据来识别和表征在2014年10月2日至2017年12月31日期间在北卡罗来纳州10家医院组成的大型医疗保健系统中接受子宫切除术治疗的18-44岁患者。我们使用卡方检验和KruskalWallis检验来比较患者的社会人口统计学和相关临床特征。和外科医生特征因患者保险付款人而异(公共，私人,无保险)。
结果：在1857名患者中（包括55％的非西班牙裔白人，30%的非西班牙裔黑人,9%西班牙裔)，75%是私人保险，17%是公共保险，7%没有保险。在公共被保险人中，月经过多更为普遍（74％对68％的总体）。在私人保险（62％）和未保险（68％）中，纤维化更为普遍。大多数私人保险患者在非学术医院接受治疗（65％），而大多数公共保险和未保险患者在学术中心接受治疗（66％和86％，分别)。公共保险和未保险的患者的中位出血（公共：7.0，未保险：9.0，私人：5.0）和疼痛（公共：6.0，未保险：6.0，私人：3.0）症状评分高于私人保险。不同付款人组的外科医生特征没有统计学差异。
结论：这项新颖的研究设计，一个基于EMR的大型病例系列的子宫切除术与医生许可数据和从非结构化临床笔记中手动提取的数据有关，能够比索赔数据更全面地识别和表征不同的生殖年龄患者群体。在这项研究的后续阶段，CHC将利用这些丰富的临床数据来调查子宫切除术的多层次驱动因素，经济上,和临床上不同系列的子宫切除术患者。
方法：我们使用结构化和抽象的EMR数据来识别和表征在2014年10月2日至2017年12月31日期间在北卡罗来纳州10家医院组成的大型医疗保健系统中接受子宫切除术治疗的18-44岁患者。我们使用卡方检验和KruskalWallis检验来比较患者的社会人口统计学和相关临床特征。和外科医生特征因患者保险付款人而异(公共，私人,无保险)。
结果：在1857名患者中（包括55％的非西班牙裔白人，30%的非西班牙裔黑人,9%西班牙裔)，75%是私人保险，17%是公共保险，7%没有保险。在公共被保险人中，月经过多更为普遍（74％对68％的总体）。在私人保险（62％）和未保险（68％）中，纤维化更为普遍。大多数私人保险患者在非学术医院接受治疗（65％），而大多数公共保险和未保险患者在学术中心接受治疗（66％和86％，分别)。公共保险和未保险的患者的中位出血（公共：7.0，未保险：9.0，私人：5.0）和疼痛（公共：6.0，未保险：6.0，私人：3.0）症状评分高于私人保险。不同付款人组的外科医生特征没有统计学差异。
结论：这项新颖的研究设计，一个基于EMR的大型病例系列的子宫切除术与医生许可数据和从非结构化临床笔记中手动提取的数据有关，能够比索赔数据更全面地识别和表征不同的生殖年龄患者群体。在这项研究的后续阶段，CHC将利用这些丰富的临床数据来调查子宫切除术的多层次驱动因素，经济上,和临床上不同系列的子宫切除术患者。
