Logic-Model_Demo_HIV&Hep_Intervention

view/download model file: Logic-Model_Demo_HIV&Hep_Intervention_N5.nlogo

WHAT IS IT?

Here is an interactive logic model, setup as inputs, outputs and outcomes. In this case a challenge for intervention planners is the interaction between spread of HIV and Hepatitis. In chapter 6 we looked at the partnering behavior, specifically serosorting, where individuals consider the HIV status of another person before they partner. They do that to reduce potential HIV transmission in the absence of using a condom. Of course this is a tricky practice because it may lead individuals to disregard an effective protective action, using a condom, which also defends against other sexually transmitted diseases, such as hepatitis. Thus one relevant intervention is hepatitis vaccination (for hepatitis A or B, which can be spread through sexual contact). Serosorting will not even prevent HIV if individuals do not know their HIV status, of course. Thus another relevant intervention is promotion of HIV testing, as is anti-retroviral therapy.

If we are interested in planning a training program to try to get health professionals to use these interventions, we can examine outcomes before and after we intervene with a training program. I once was asked to help develop a logic model for precisely that type of training program, so I drew up a traditional logic model and then an interactive version of the same. This model is the interactive version (see chapter 10 of my book for more details). The interactive model is also able to show more behavioral variables, such as serosorting, as compared to a static logic model. You can set various pre-intervention levels of knowledge of HIV status, as well as likelihood to couple, length of relationship, and likelihood to serosort.

Try different settings for serosorting. Try intervening in different ways. When you switch on “intervene” you will see the doctor move from the west to the east end of the neighborhood. You can use the slider to give some individuals a preference for the east end.

I have it set so that only individuals coming near the doctor, indicating receipt of health care, can receive the intervention. This is a healthcare based intervention. Thus sliders for the intervention apply to those who actually receive some health care.

This model builds on the HIV model that comes with Netlogo and includes some of the same sliders including:

AVERAGE-COUPLING-TENDENCY (0 - 10) determines the tendency of the individuals to become involved in couples (as stated earlier, all couples are presumed to be sexually active). When the AVERAGE-COUPLING-TENDENCY slider is set at zero, coupling is unlikely, although (because tendencies are assigned in a normal distribution) it is still possible. Note that when deciding to couple, both individuals involved must be “willing” to do so, so high coupling tendencies in two individuals are actually compounded (i.e. two individuals with a 50% chance of coupling actually only have a 25% of coupling in a given tick).

The slider AVERAGE-COMMITMENT (1 - 200) determines how long individuals are likely to stay in a couple (in weeks). Again, the tendencies of both individuals in a relationship are considered; the relationship only lasts as long as is allowed by the tendency of the partner with a shorter commitment tendency.

The slider AVERAGE-CONDOM-USE (0 - 10) determines the tendency of individuals in the population to practice safe sex. If an individual uses a condom, it is assumed in this model that no infection by HIV is possible. Note that this tendency (like the others) is probabilistic at several levels. For instance, when AVERAGE-CONDOM-USE is set to 9, most of the individuals have a CONDOM-USE value of 9, although some have CONDOM-USE values of 8 or 10, and fewer yet have CONDOM-USE values of 7 or 11 (11 would be off-scale and the same as 10 for all practical purposes). Also, an individual with a CONDOM-USE value of 9 will still sometimes not choose to use a condom (10% of the time, roughly). Simulation of condom-use is further complicated by the fact that if one partner “wants” to use a condom while the other partner does not, the couple does not use condoms. This characteristic of the model is representative of the dynamics of some sexual relations, but not others. The decision was somewhat arbitrary, and the user is invited to play with this characteristic and others in the “Extending the Model” section of this tab.

The slider AVERAGE-TEST-FREQUENCY (0 - 2) is the final slider of the interface. It determines the average frequency of an individual to get tested for HIV in a one-year time span. Set at 1.0, the average person will get tested for HIV about once a year. Set at 0.2, the average individual will only get tested about every five years. This tendency has significant impact because the model assumes that individuals who know that they are infected always practice safe sex, even if their tendency as healthy individuals was different. Again, this characteristic of the model is only represented of the behaviors of some individuals. The model was designed in this way to highlight the public health effects associated with frequent testing and appropriate responses to knowledge of one’s HIV status. To explore the impact of alternative behaviors on public health, the model code can be changed relatively painlessly. These changes are described in the section, “Extending the Model.”

The model’s plot draws a line graph showing the total number of uninfected individuals (green), infected individuals whose positive status is not known (blue), and infected individuals whose positive status is known (red).

CREDITS AND REFERENCES

I use this model in chapter 10 of my book. This model builds significantly on the HIV model that comes with NetLogo. That model gives special thanks to Steve Longenecker for model development.

To refer to this model in academic publications, please use: Wilensky, U. (1997). NetLogo AIDS model. http://ccl.northwestern.edu/netlogo/models/AIDS. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.