Writing a custom intervention

As explained earlier, an intervention will typically affect the exposure distribution of a risk factor by modifying one (or more) of:

• The rate(s) that affect the exposure (e.g., uptake of tobacco smoking);

• The prevalence of exposure categories (e.g., moving people from one exposure category to another); and

• The relative risk(s) associated with an exposure category.

Note

Interventions may also directly affect chronic and acute diseases, by modifying any of the rates associated with those diseases. This is very similar to modifying any of the rates that affect the exposure of a risk factor; the only difference is the choice of which rate(s) will be affected.

Structure of an intervention component

An intervention component will comprise the following methods:

• A constructor (__init__) that will normally accept two arguments:

  • The self parameter (a reference to the component instance); and

  • A name that will be used to identify this intervention, and which may be used in the configuration section of a simulation definition in order to define settings for this intervention.

• A setup(self, builder) method that will:

  • Load any required input value or rate tables.

  • Read any intervention-specific settings from builder.configuration, such as the year at which the intervention will come into effect.

  • Register value and/or rate modifiers (if required).

  • Register a time-step event handler to, e.g., move people from one exposure category to another (if required).

• Some number of value and/or rate modifiers (if required).

• The time-step event handler (if required).

Example of an intervention component

As an example, we will walk through each of these methods for the TobaccoEradication intervention. This intervention is quite simple, because it doesn’t need to load any input data tables, and it has an all-or-nothing effect on a risk factor rate.

Recall that this intervention is controlled by a single configuration setting:

configuration:
    tobacco_eradication:
        year: 2011

The constructor

This intervention is currently hard-coded to modify the 'tobacco' risk factor, which it stores in self.exposure.

The setup method

The setup() method performs a several necessary house-keeping tasks:

• It retrieves the year at which the intervention comes into effect (specified in the configuration section, as shown above) and stores it in self.year.

• It stores the simulation clock in self.clock, so that it can detect when this intervention comes into effect.

• It registers a modifier for the tobacco_intervention.incidence rate (i.e., the uptake rate in the intervention scenario).

• It registers a modifier for the tobacco_intervention.remission rate (i.e., the cessation rate in the intervention scenario).

The incidence modifier

The adjust_inc_rate() method, which was registered as a modifier for the tobacco_intervention.incidence rate, will set the rate to zero once the intervention is active. Recall that self.year is the year at which this intervention comes into effect.

Note

Once this intervention becomes active, this rate modifier applies an effect on every time-step.

The remission modifier

The adjust_rem_rate() method, which was registered as a modifier for the tobacco_intervention.remission rate, will set the rate to one once the intervention is active. This will have the effect of moving all of the people in the currently smoking exposure category to the 0 years post-cessation exposure category. Recall that self.year is the year at which this intervention comes into effect.

Note

Once this intervention becomes active, this rate modifier applies an effect on every time-step.