Denominators Matter: Understanding Medical Encounter Frequency and Its Impact on Surveillance Estimates Using EHR Data

N. Cocoros, Aileen Ochoa, Karen Eberhardt, Bob Zambarano, M. Klompas
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引用次数: 7

Abstract

Background: There is scant guidance for defining what denominator to use when estimating disease prevalence via electronic health record (EHR) data. Objectives: Describe the intervals between medical encounters to inform the selection of denominators for population-level disease rates, and evaluate the impact of different denominators on the prevalence of chronic conditions. Methods: We analyzed the EHRs of three practices in Massachusetts using the Electronic medical record Support for Public Health (ESP) system. We identified adult patients’ first medical encounter per year (2011–2016) and counted days to next encounter. We estimated the prevalence of asthma, hypertension, obesity, and smoking using different denominators in 2016: ≥1 encounter in the past one year or two years and ≥2 encounters in the past one year or two years. Results: In 2011–2016, 1,824,011 patients had 28,181,334 medical encounters. The median interval between encounters was 46, 56, and 66 days, depending on practice. Among patients with one visit in 2014, 82–84 percent had their next encounter within 1 year; 87–91 percent had their next encounter within two years. Increasing the encounter interval from one to two years increased the denominator by 23 percent. The prevalence of asthma, hypertension, and obesity increased with successively stricter denominators – e.g., the prevalence of obesity was 24.1 percent among those with ≥1 encounter in the past two years, 26.3 percent among those with ≥1 encounter in the last one year, and 28.5 percent among those with ≥2 encounters in the past one year. Conclusions: Prevalence estimates for chronic conditions can vary by >20 percent depending upon denominator. Understanding such differences will inform which denominator definition is best to be used for the need at hand.
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分母很重要:了解医疗接触频率及其对使用电子病历数据的监测估计的影响
背景:在通过电子健康记录(EHR)数据估计疾病流行率时,很少有关于定义使用什么分母的指导。目的:描述两次就诊之间的间隔,以告知人群疾病率的分母选择,并评估不同分母对慢性病患病率的影响。方法:我们使用公共卫生电子病历支持(ESP)系统分析了马萨诸塞州三家诊所的EHR。我们确定了成年患者每年(2011-2016年)的第一次就诊,并计算了下一次就诊的天数。我们在2016年使用不同的分母估计了哮喘、高血压、肥胖和吸烟的患病率:在过去一年或两年内≥1次,在过去一两年内≥2次。结果:2011-2016年,1824011名患者共有28181334次就诊。两次相遇的中位间隔时间分别为46、56和66天,具体取决于练习情况。在2014年一次就诊的患者中,82%-84%的患者在一年内再次就诊;87–91%的人在两年内有了下一次邂逅。将相遇间隔从一年增加到两年,分母增加了23%。哮喘、高血压和肥胖的患病率随着分母的不断严格而增加——例如,在过去两年中,≥1次发作的患者中,肥胖的发病率为24.1%,在过去一年中≥1次的患者中为26.3%,在过去1年中≥2次发作的人中为28.5%。结论:根据分母的不同,慢性病的患病率估计值可能相差20%以上。了解这些差异将告知哪种分母定义最适合用于手头的需求。
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