Changes in health information technology during the last decade and an increasing demand for data sharing and transparency have increased public access to health-related data. In particular, several web-based tools have been developed to share local, state, and national health data with audiences ranging from the general public to public health agencies and epidemiologic researchers (1–10) (Table 1). These websites and interactive tools are intended to help communities throughout the United States understand the health of their county or state and to prioritize interventions. For example, County Health Rankings and America’s Health Rankings summarize and display data on factors important to health and health management (2,5). However, the data used by these sites are collected from cross-sectional surveys of various groups of people at single points in time (eg, the Behavioral Risk Factor Surveillance System [BRFSS]) (11,12). These sites have data that can be summarized individually, by demographic characteristics, and by geographic region, but exploration of associations between different conditions or across data sets is not possible because the data are rarely linked to identifiable individuals. In addition, survey data are self-reported, and the number and type of health conditions included are limited.

Other interfaces are available from the Centers for Medicare & Medicaid Services (CMS Data Navigator) (7), the Dartmouth Atlas of Health Care (8), and the Agency for Healthcare Research and Quality’s Healthcare Cost and Utilization Project website (HCUPnet) (9). These websites allow users to summarize Medicare and Medicaid claims information (7,8) as well as information from state and national inpatient and emergency department databases (13). The CMS Data Navigator provides access to published reports on various topics (7), and users can explore the prevalence and co-occurrence of 20 chronic conditions (through the Medicare Chronic Conditions Dashboard) (14,15). The Dartmouth Atlas website tools also aggregate and summarize Medicare data but allow for more customized queries focused on health care utilization and outcomes (8). However, these sites provide limited information on specific diseases and conditions, particularly those that are rare. In addition, Medicare predominantly serves the population aged 65 years or older. Therefore, these sites are of limited use for understanding the health of younger populations.

Finally, the HCUPnet website allows users to explore detailed data available from State and National Inpatient Databases, the State Ambulatory Surgery and Services Database, and State and National Emergency Department Databases (9). Information is available across all ages and both sexes, and on all conditions that occur during an inpatient or emergency department visit. However, these data sets lack information on outpatient visits, and the interactive tools do not allow users to explore associations across conditions.

Our objective was to develop an interactive, web-based tool, the Rochester Epidemiology Project (REP) Data Exploration Portal (DEP), to display data on the prevalence and co-occurrence of 717 conditions from the expanded REP medical records-linkage system, which collects data from participating health care providers in in a 27-county region of southern Minnesota and western Wisconsin (16–18).

Development of the REP DEP took place from May 2016 through May 2017. The Mayo Clinic and Olmsted Medical Center institutional review boards approved this project. We designed the tool to allow users to access summary information on the health conditions of people in a 27-county region of southern Minnesota and western Wisconsin. First, we coded information on 717 conditions. We then calculated 1) the prevalence of each selected condition, 2) the prevalence of combinations of 2 conditions (dyads), and 3) observed-to-expected ratios (OERs) to measure the excess co-occurrence of dyads.

The first version of the REP DEP was launched in May 2017 and can be accessed at http://rochesterproject.org/portal/. To search for a condition, users can click on the box “Characteristic A selection” and start typing the text of the condition of interest. The selection list will narrow to include conditions matching the typed text. The second condition, Characteristic B, is selected in the same way. Users can display results by using the “Prevalence” tab and the “Geography” tab.

We developed the interactive, web-based REP DEP to display the prevalence and co-occurrence of 717 diseases and conditions recorded in the E-REP records-linkage system. We expect the REP DEP to be useful to local residents, health care practitioners, and local administrators in understanding patterns of disease in this Midwestern region. The data may also serve as a benchmark for other communities and may provide a cost-effective way for researchers to explore whether an association between 2 conditions exists before conducting a full epidemiologic study.

The REP DEP includes data on all conditions that come to medical attention, regardless of whether the care was delivered in the outpatient, inpatient, or emergency department setting. As such, it overcomes limitations of other websites that include only a limited number of conditions or only data from inpatient or emergency department settings (2–6,13), and it allows users to obtain prevalence estimates on both common and rare conditions and to include both inpatient and outpatient diagnoses. Second, the REP DEP includes data for all age groups, overcoming the limitations of websites that rely predominantly on Medicare data (7,8). We expect REP DEP prevalence estimates to be particularly useful for public health and care delivery organizations in this 27-county region in ranking their most urgent community health priorities. For example, tax-exempt hospitals must conduct a community health needs assessment every 3 years in compliance with the Patient Protection and Affordable Care Act, and they must develop a community health improvement plan to address the most urgent priorities (29). The REP DEP can identify the prevalence of a wide array of medical conditions, and, in the future, will provide a way to determine whether the prevalence of key conditions changes over time.

REP DEP data are also linked at the person-level, which allows users to explore associations between conditions. This type of data exploration is not possible on other websites that aggregate de-identified data from different sources and populations (2–6). In addition, the underlying data included in the REP DEP are linked to patient identifiers through the E-REP research infrastructure (18). With appropriate approvals, the E-REP can be leveraged for recruiting study participants, and these participants may be followed via their linked medical records to cost-effectively assess outcomes that come to medical attention. Therefore, the REP DEP offers a method for determining whether a given community includes a sufficient number of potential participants for a community-based clinical trial (30).

This study has limitations. Data are available for 61% of the population residing in the 27-county region. Participants may differ from nonparticipants, and prevalence estimates may be biased. Conditions that are diagnosed and treated at health care providers that do not participate in the E-REP may be missed, and the true prevalence of some conditions may be underestimated. The age and sex distribution of the population included in the REP DEP is similar to US Census estimates for the 27-county region, but participants may differ from nonparticipants in other factors that influence health (eg, socioeconomic status).

Second, we informally compared REP DEP prevalences for 5 common chronic conditions with 2015 prevalence estimates for the state of Minnesota from the BRFSS (31); however, we did not perform formal statistical testing for the differences. Prevalence estimates were similar for asthma (REP DEP, 8% vs BRFSS, 7%) and depression (REP DEP, 14% vs BRFSS, 19%). However, REP DEP estimates were higher for diabetes (REP DEP, 14% vs BRFSS, 8%), and lower for arthritis (REP DEP, 15% vs BRFSS, 22%) and hyperlipidemia (REP DEP, 24% vs BRFSS, 32%). These discrepancies highlight the fact that different data collection methods are likely to yield different prevalence estimates. Methodologic differences between the BRFSS and the REP DEP preclude a more formal comparison. The BRFSS estimates were obtained from adult participants reporting whether they had ever been told that they had the condition of interest. By contrast, the REP DEP prevalence estimates were obtained from data on participants of all ages whose medical record had at least one ICD-9 code of interest in a 5-year time frame. The underlying ICD-9 codes were obtained from billing data and were not validated. Therefore, the prevalences and OERs generated by the REP-DEP may deviate from the truth. This limitation is common to all publicly accessible databases. In addition, the sensitivity and specificity of a single ICD-9 code for a condition of interest varies (32,33). Therefore, further validation studies may be necessary, depending on the condition of interest. Finally, the BRFSS estimates were for the adult population of the entire state of Minnesota, whereas the REP DEP estimates were for persons of all ages residing in a region that includes southern Minnesota and western Wisconsin. Inclusion of children in the estimates will underestimate the prevalence of chronic diseases that predominantly affect adults. However, variability in prevalence estimates may also reflect true prevalence differences between the REP DEP and BRFSS populations.

Third, ICD-9 codes were grouped into larger categories. Specific diagnoses may have been overly aggregated, resulting in the inability to test for associations of interest. For example, the diagnostic codes for Alzheimer’s disease are part of the larger category of “delirium, dementia, and amnestic and other cognitive disorders.” However, Alzheimer’s disease is a major research focus for many investigators; therefore, we included Alzheimer’s disease as an option in our search tool. In the second release of the REP DEP (January 2018), we included a series of more specific conditions. Finally, once we have 3 full years of data accumulation (2014–2016), we will add trend graphs to explore increases or decreases in the prevalence of conditions across calendar years.

The REP DEP covers a geographically defined Midwestern population, and the prevalence of medical conditions will be different in other United States communities, depending on the characteristics of the underlying population. However, the REP DEP data may still serve as a useful benchmark for other communities. In particular, it is often difficult to obtain baseline prevalence data for rare conditions. The REP DEP provides prevalence estimates for all conditions in this population, and it offers a free, rapid way to obtain comparison data. The REP DEP also provides an example of how other communities might leverage and display their own data to inform local planning efforts.

Finally, the underlying biological processes that lead to the development and co-occurrence of diseases and conditions are less likely to vary from community to community. Therefore, the OERs that can be obtained through the REP DEP are likely generalizable to other populations. As such, these data provide an avenue for researchers to determine whether 2 conditions are associated before conducting a larger, resource-intensive epidemiologic study.

The REP DEP provides a rapid, interactive, free-of-charge method to examine the prevalence and co-occurrence of 717 diseases and conditions in a large, Midwestern population. The REP DEP will be useful to local communities for understanding the prevalence of virtually all conditions in this region. In addition, these data may serve as a benchmark for other communities, particularly for rare conditions. The REP DEP can provide preliminary data for investigators who are considering further studies of the co-occurrence of diseases or are assessing the feasibility of a community-based clinical trial.

In January 2018, we released a new version of the REP DEP. This updated version of the portal allows users to choose from among 1,376 characteristics, including diagnosis-based medical conditions, procedures and surgeries, prescription medications, and demographic characteristics (eg, race, ethnicity, smoking status, overweight and obesity categories). In addition, users may now choose to define a characteristic as occurring in either the 5-year period before prevalence date or in a 1-year period before prevalence date. These updates to the REP DEP give users more flexibility to explore the relationships between characteristics. Complete details can be found in the updated REP DEP User Manual on the portal Documentation tab (http://www.rochesterproject.org/portal/).