Economics of the Obesity Epidemic – Extracting Knowledge with Advanced Text Analytics

July 15 2014

In the current competitive marketplace for healthcare, pharmaceutical and medical technology companies must be able to demonstrate clinical and economic evidence of benefit to providers, healthcare decision-makers and payers.

Now more than ever, pricing pressure and regulatory restrictions are generating increased demand for this kind of outcomes evidence.

Health Economics and Outcomes Research (HEOR) aims to assess the direct and indirect health care costs associated with a disease or a therapeutic area, and associated interventions in real-world clinical practice.

These costs include:

  • Direct economic loss
  • Economic loss through hospitalization
  • Indirect costs from loss of wider societal productivity

The availability of increasing amount of data on patients, prescriptions, markets, and scientific literature combined with the wider use of comparative effectiveness make traditional keyword based search techniques ineffectual. I2E can provide the starting point for efficiently performing evidence based systematic reviews over very large sets of scientific literature, enabling researchers to answer questions such as:

• What is the economic burden of disease within the healthcare system? Across states, and globally?

• Does XYZ new intervention merit funding? What are the economic implications of its use?

• How do the incremental costs compare with the anticipated benefits for specific patient groups?

• How does treatment XYZ affect quality of life? Activities of daily living? Health status indicators? Patient satisfaction?

A recent project looking at the economics of obesity used I2E to search all 23 million abstracts in Medline for research on the incidence of comorbid diseases, with associated information on patient cohort, geographic location, severity of disease, and associated costs (e.g. hospitalisation cost, treatment, etc.).

From the I2E output, more advanced visual analytics can be carried out. For example, the pie chart shows the prevalence of the various comorbid diseases (from 2013 Medline abstracts with both HEOR terms, obesity and a comorbid disease), showing the high frequency of hypertension and various other cardiovascular diseases.

Another view of the same extracted intelligence shows the geographic spread of health economics and obesity research, with a predominance across northern America, but also data from China and Brazil, for example.


Prevalence of cardiovascular co-morbid diseases



Geographic view of HEOR research, mined from Medline from 2013

If you are interested in getting a better understanding of the power of advanced text analytics for HEOR, please contact us.