Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905. Email:
Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota.
Prev Chronic Dis. 2018 Apr 12;15:E42. doi: 10.5888/pcd15.170242.
The goal of this project was to develop an interactive, web-based tool to explore patterns of prevalence and co-occurrence of diseases using data from the expanded Rochester Epidemiology Project (E-REP) medical records-linkage system.
We designed the REP Data Exploration Portal (REP DEP) to include summary information for people who lived in a 27-county region of southern Minnesota and western Wisconsin on January 1, 2014 (n = 694,506; 61% of the entire population). We obtained diagnostic codes of the International Classification of Diseases, 9th edition, from the medical records-linkage system in 2009 through 2013 (5 years) and grouped them into 717 disease categories. For each condition or combination of 2 conditions (dyad), we calculated prevalence by dividing the number of persons with a specified condition (numerator) by the total number of persons in the population (denominator). We calculated observed-to-expected ratios (OERs) to test whether 2 conditions co-occur more frequently than would co-occur as a result of chance alone.
We launched the first version of the REP DEP in May 2017. The REP DEP can be accessed at http://rochesterproject.org/portal/. Users can select 2 conditions of interest, and the REP DEP displays the overall prevalence, age-specific prevalence, and sex-specific prevalence for each condition and dyad. Also displayed are OERs overall and by age and sex and maps of county-specific prevalence of each condition and OER.
The REP DEP draws upon a medical records-linkage system to provide an innovative, rapid, interactive, free-of-charge method to examine the prevalence and co-occurrence of 717 diseases and conditions in a geographically defined population.
本项目的目标是开发一个交互式、基于网络的工具，使用来自扩展的罗切斯特流行病学项目（E-REP）病历链接系统的数据来探索疾病的流行和共现模式。
我们设计了 REP 数据探索门户（REPDEP），以包括 2014 年 1 月 1 日居住在明尼苏达州南部和威斯康星州西部的 27 个县的人群的汇总信息（n=694506；占总人口的 61%）。我们从病历链接系统中获取了 2009 年至 2013 年（5 年）的国际疾病分类第 9 版诊断代码，并将其分为 717 种疾病类别。对于每种疾病或两种疾病的组合（对偶），我们通过将指定疾病的人数（分子）除以人群中的总人数（分母）来计算患病率。我们计算了观察到的与预期的比值（OER），以检验两种疾病是否比仅因机会而共同发生的频率更高。
我们于 2017 年 5 月推出了 REPDEP 的第一个版本。REPDEP 可在 http://rochesterproject.org/portal/ 上访问。用户可以选择两种感兴趣的疾病，REPDEP 会显示每种疾病和对偶的总体患病率、年龄特异性患病率和性别特异性患病率。还显示了总体和按年龄和性别划分的 OER 以及每种疾病和 OER 的县特异性患病率地图。
REPDEP 利用病历链接系统提供了一种创新的、快速的、交互式的、免费的方法，用于检查地理定义人群中 717 种疾病和状况的流行和共现。
