■描述IRIS®(视觉智能研究)注册表中缺失的社会人口统计数据的患病率,并确定与缺失的社会人口统计数据相关的实践水平特征。
■横断面研究。
■在2020年12月31日之前参加IRIS注册的实践中遇到临床问题的所有患者。
■我们描述了每个社会人口统计学变量(年龄,性别,种族,种族,地理位置,保险类型,和吸烟状况)。每个为注册提供数据的实践都根据患者数量进行分类,医生的数量,地理位置,患者就诊频率,和患者人口统计。
■多变量线性回归用于描述实践水平特征与缺失患者水平社会人口统计学数据的关联。
这项研究包括了66名477.365患者的电子健康记录,这些患者在参与IRIS注册的3306诊所接受护理。每次练习的患者人数中位数为11.415(四分位距:5849-24148),每次练习的医生人数中位数为3(四分位距:1-7)。出生年份患者社会人口统计学数据缺失的患病率为0.1%,0.4%的性别,种族占24.8%,种族占30.2%,3位数邮政编码为2.3%,国家占14.8%,吸烟状况为5.5%,险种为17.0%。缺失数据的患病率随着时间的推移而增加,并且在州一级有所不同。缺少种族数据与每位患者就诊次数较少的实践相关(P<0.001),照顾更大的非私人保险患者群体(P=0.001),位于城市地区(P<0.001)。频繁的患者就诊与较低的种族缺失患病率相关(P<0.001)。种族(P<0.001),和保险(P<0.001),但缺失吸烟状况的患病率较高(P<0.001)。
■缺少种族存在地理和时间趋势,种族,和IRIS注册表中的保险类型数据。几个实践层面的特点,包括练习尺寸,地理位置,和患者群体,与缺失的社会人口统计数据有关。虽然丢失数据的普遍性和模式可能会在IRIS注册表的未来版本中发生变化,仍然需要开发标准化方法,以最大限度地减少潜在的偏倚来源,并确保整个研究的可重复性.
■专有或商业披露可在本文末尾的脚注和披露中找到。
UNASSIGNED: To describe the prevalence of missing sociodemographic data in the IRIS® (Intelligent Research in Sight) Registry and to identify practice-level characteristics associated with missing sociodemographic data.
UNASSIGNED: Cross-sectional study.
UNASSIGNED: All patients with clinical encounters at practices participating in the IRIS Registry prior to December 31, 2020.
UNASSIGNED: We describe geographic and temporal trends in the prevalence of missing data for each sociodemographic variable (age, sex, race, ethnicity, geographic location, insurance type, and smoking status). Each practice contributing data to the registry was categorized based on the number of patients, number of physicians, geographic location, patient visit frequency, and patient population demographics.
UNASSIGNED: Multivariable linear regression was used to describe the association of practice-level characteristics with missing patient-level sociodemographic data.
UNASSIGNED: This study included the electronic health records of 66 477 365 patients receiving care at 3306 practices participating in the IRIS Registry. The median number of patients per practice was 11 415 (interquartile range: 5849-24 148) and the median number of physicians per practice was 3 (interquartile range: 1-7). The prevalence of missing patient sociodemographic data were 0.1% for birth year, 0.4% for sex, 24.8% for race, 30.2% for ethnicity, 2.3% for 3-digit zip code, 14.8% for state, 5.5% for smoking status, and 17.0% for insurance type. The prevalence of missing data increased over time and varied at the state-level. Missing race data were associated with practices that had fewer visits per patient (P < 0.001), cared for a larger nonprivately insured patient population (P = 0.001), and were located in urban areas (P < 0.001). Frequent patient visits were associated with a lower prevalence of missing race (P < 0.001), ethnicity (P < 0.001), and insurance (P < 0.001), but a higher prevalence of missing smoking status (P < 0.001).
UNASSIGNED: There are geographic and temporal trends in missing race, ethnicity, and insurance type data in the IRIS Registry. Several practice-level characteristics, including practice size, geographic location, and patient population, are associated with missing sociodemographic data. While the prevalence and patterns of missing data may change in future versions of the IRIS registry, there will remain a need to develop standardized approaches for minimizing potential sources of bias and ensure reproducibility across research studies.
UNASSIGNED: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.