Ross Connor, Ivanov Alexander, Elze Tobias, Miller Joan W, Lum Flora, Lorch Alice C, Oke Isdin
Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
American Academy of Ophthalmology, San Francisco, California.
Ophthalmol Sci. 2024 Apr 30;4(6):100542. doi: 10.1016/j.xops.2024.100542. eCollection 2024 Nov-Dec.
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.
Cross-sectional study.
All patients with clinical encounters at practices participating in the IRIS Registry prior to December 31, 2020.
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.
Multivariable linear regression was used to describe the association of practice-level characteristics with missing patient-level sociodemographic data.
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 ( < 0.001), cared for a larger nonprivately insured patient population ( = 0.001), and were located in urban areas ( < 0.001). Frequent patient visits were associated with a lower prevalence of missing race ( < 0.001), ethnicity ( < 0.001), and insurance ( < 0.001), but a higher prevalence of missing smoking status ( < 0.001).
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.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
描述IRIS®（智能视觉研究）注册中心社会人口统计学数据缺失的患病率，并确定与社会人口统计学数据缺失相关的医疗机构层面特征。
横断面研究。
2020年12月31日前在参与IRIS注册中心的医疗机构接受临床诊疗的所有患者。
我们描述了每个社会人口统计学变量（年龄、性别、种族、族裔、地理位置、保险类型和吸烟状况）数据缺失患病率的地理和时间趋势。根据向注册中心提供数据的每个医疗机构的患者数量、医生数量、地理位置、患者就诊频率和患者人口统计学特征进行分类。
采用多变量线性回归来描述医疗机构层面特征与患者层面社会人口统计学数据缺失之间的关联。
本研究纳入了在参与IRIS注册中心的3306家医疗机构接受治疗的66477365例患者的电子健康记录。每个医疗机构的患者中位数为11415例（四分位间距：5849 - 24148例），每个医疗机构的医生中位数为3名（四分位间距：1 - 7名）。患者社会人口统计学数据缺失的患病率分别为：出生年份0.1%、性别0.4%、种族24.8%、族裔30.2%、三位数字邮政编码2.3%、州14.8%、吸烟状况5.5%、保险类型17.0%。数据缺失的患病率随时间增加且在州层面存在差异。种族数据缺失与每位患者就诊次数较少的医疗机构相关（<0.001）、照顾的非私人保险患者群体较大的医疗机构相关（=0.001）以及位于城市地区的医疗机构相关（<0.001）。患者频繁就诊与种族（<0.001）、族裔（<0.001）和保险（<0.001）数据缺失的患病率较低相关，但与吸烟状况数据缺失的患病率较高相关（<0.001）。
IRIS注册中心在种族、族裔和保险类型数据缺失方面存在地理和时间趋势。包括医疗机构规模、地理位置和患者群体在内的几个医疗机构层面特征与社会人口统计学数据缺失相关。虽然IRIS注册中心未来版本中数据缺失的患病率和模式可能会改变，但仍需要制定标准化方法以尽量减少潜在的偏差来源，并确保跨研究的可重复性。
在本文末尾的脚注和披露中可能会发现专有或商业披露信息。
