>> The Epiomic Database

The Epiomic Database

On April 25, 2019, Tags Epiomic Database, Epidemiology

The Epiomic database is a sources of robust, evidence-based patient populations for specific diseases, hospital admission and healthcare procedures.

The database has been providing epidemiology data for over 6 years now, and in that time we have expanded our coverage to 197 disease areas in over 19 major global markets. But we are not stopping there, in the next few months we will have over 90% coverage in 8 new major markets and an extra 3 new diseases (pushing us over 200!).

Each disease area can be segmented by gender and 5-year age cohort as well as into ‘sub-populations’ such as clinical characteristics, co-morbid conditions and biomarkers. Some conditions may have multiple layers of segmentation which can be used to draw out rare diseases and niche patient populations. For Example: Lung Cancer -> Non-small cell lung cancer -> Adenocarcinoma -> K-Ras mutation.

All of the data can be backed up by relevant evidence. The majority of the sources we use come from publicly available secondary research such as national surveys, registries, research studies and charity/NPO. The data gathered is also subjected to a rigorous evaluation process that scrutinises disease definition and diagnostic method, sample size, date of the study and more.

There can be many challenges when producing patient population data so the first step in our methodology is always to build an extensive understanding of the disease area, this includes disease definition and classification, aetiology, treatment pathways, symptoms and more. This ensures our solutions will always be the most relevant and sensible for that disease and situation.

A few typical challenges we face when producing patient populations include:

  • Missing data for certain countries or whole regions
  • Increasing/decreasing patient populations
  • Nonuniform diagnostic criteria or disease definition

Using our disease knowledge, we can overcome these challenges by selecting the most appropriate surrogate for missing data, dynamically forecast our patient populations or using the most clinically relevant definitions.


Gregg Higgins

Senior Epidemiology Analyst

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