DISEASE MONITORING TOOL

In developing a new intervention it is important to understand its dynamic impact on the disease. Only this makes it possible to assess the true health economics consequences over time. Based on epidemiological methods and principles we have deviced such a model, the "BOX model", which has proven its global applicability.

We describe the dynamics of a disease

In principle we conduct accounting for the patient population. With outset in the patient population at risk of developing a given disease we calculate the annual input to the patient population (new patients per year). From this we deduct the annual output from the patient population (deaths and recoveries, as relevant). Adding input and subtracting output from the prevalence at the beginning of the year yields the prevalence ultimo the year. By repeating this accountance year by year the trends over time in input and output rates as well as the resulting prevalence will be obtained.
The patient population may be stratified in sub-groups by, for example, treatment status so that internal transitions between the various states can be monitored over time
With this model we can estimate the value of any given intervention by its contribution to the parameters in the intervention.

The model into context

Complications represent a significant contribution to the costs of chronic diseases and it is relevant to monitor the dynamics. As an example patients with diabetes were stratified into

  • "no complications" (status 0),
  • "minor complications" (status 1)
  • and "major complications" (status 2)

By assuming that a patient of any status group can either move up one step (e.g. 0 to 1) or die, and based on epidemiology we can model the number of Prevalent Patients for each status group at the base scenario and at the intervention scenario. The comparison of the scearios will lead to an assessment of the health economics of the intervention.

 

Global Application

A segmentation based on different pharmaceutical regimens in a progressive chronic disease, this model will predict the pharmaco-epidemiological aspects of new or more intensive pharmaceutical intervention as we have demonstrated for Diabetes, Fyns County, Denmark (Source: Re-analysis of OPED data from Støvring H et al, The Lancet 2003).

Similar health economic problems have been solved in Primary Lung Cancer (PLC), Renal Failure and Pacemaker transplantations.

Selected references to the Box Model

Andersen C, Green A, Madsen G, Arnsbo P
The epidemiology of pacemaker implantations in Fyn County, Denmark. Pace 14: 1614-1621, 1991

Green A, Sjølie AK, Eshøj O
Trends in the epidemiology of IDDM during 1970-2020 in Fyn County, Denmark. Diabetes Care 19: 801-806, 1996

Green A, Emneus M, Christiansen T, Björk S
The social impact of diabetes mellitus and diabetes care. Report 1: Methodology. Health Economics Papers 2005:4. University of Southern Denmark

Green A, Emneus M, Christiansen T, Björk S, Kristensen JK
The social impact of diabetes mellitus and diabetes care. Report 2: Type 1 diabetes in Denmark year 2001. Health Economics Papers 2006:1. University of Southern Denmark

Green A, Emneus M, Christiansen T, Björk S, Kristensen JK
The social impact of diabetes mellitus and diabetes care. Report 3: Type 2 diabetes in Denmark year 2001. Health Economics Papers 2006:2. University of Southern Denmark