Modelling lifestyle characteristics and lifestyle-related diseases
problematic alcohol use in the Dutch population. These topics were chosen because they are the largest cause of disease burden in the Netherlands. Aims, goals and measures are laid down in the agreement with a time period until 2040. For example, one aim is to reduce the percentage of smoking adults to below 5% in 2040.
It was also agreed that the National Institute for Public Health and the Environment (RIVM) would publish a progress report every year and asses the achievability of the aims based on actual progress (through quantitative projections) once every four years. The latter assessment is the subject of our current research.
Our aim is to estimate the impact of the prevention measures within the NPA and project those for the time period until 2040. We are developing a microsimulation public health model, the Life Course Disease Model (LCDM), which predicts the life course of individuals aged 18 and older who together form the adult Dutch population. Each individual has a number of personal characteristics: gender, age and education level. In addition, LCDM models the lifestyle characteristics (BMI, smoking behavior and alcohol use) for each person. Furthermore, individuals may develop type II diabetes or other lifestyle-related diseases and individuals may die. LCDM also takes into account demographic factors such as a newborn individuals and migration.
In order to be able to simulate the life course of each individual, the model registers how all his or her characteristics are developing. (S)he grows a year older, but also (s)he has a chance to, for example, start or stop smoking, gain or lose weight or develop type II diabetes. The probabilities of these annual transitions in risk factors and (disease/mortality) events may in turn depend on gender, age, education and calendar year (and on lifestyle risk factors in case of developing related diseases). All probabilities that the model uses for the development of characteristics are determined on the basis of data and/or on the basis of scientific literature. We are going to use data from Lifelines to determine some of these probabilities for annual transitions in smoking behavior and alcohol use. We will also analyze probabilities of risk factors related to cardiovascular disease, such as cholesterol and blood pressure.