by Mandy Izzo
Sr. Science Writer, Institute for Disease Modeling
I count today as a “win” in terms of finding great sessions to attend: I was able to balance novelty and familiarity of topics in a way that was entirely satisfying. I attended a session on HIV and tropical co-infections, a topic that I have some background in. It was a great session to attend, as the speakers provided insightful examinations into risk factors of HIV that were very different from what I’ve heard before. An analysis of bednet use (a well-documented malaria intervention) demonstrated a correlation with HIV risk—namely, that those people less likely to use bednets faced higher risks of developing HIV. While it is not surprising that people engaging in risky behaviors in one situation are more likely to engage in risky behaviors in other situations, I did find myself surprised by the integration of data across disease types. So many of the world’s most burdensome diseases occur in the same area—people at risk of contracting malaria are often also at risk for contracting HIV; so why not utilize measures that can function for both problems? Why not combine efforts into massive intervention campaigns? Or at the very least, leverage the data that’s abundantly available from one group to facilitate progress in another.
This concept of “interdisciplinary interventions” has a great deal of appeal to me. Malaria eradication efforts have gained huge traction across much of Africa, and there are quite a few very successful bednet distribution programs. Data on ownership and usage of these nets is regularly collected, so if it can also be used to help focus HIV intervention programs, what a fantastic bonus! For health workers engaging with communities, they can focus efforts in multiple areas of public health. Perhaps intervention programs will enjoy greater success through combined efforts, without a drastic increase in the effort required!
Resource acquisition and allocation is always an issue in public health, so perhaps that is why I was so struck by the concept of combining efforts and leveraging data in novel ways. Later in this session, another researcher presented results examining non-clinical factors associated with HIV mortality in Africa. Clinical data can be difficult to obtain, and there are many resource-poor areas where having social or demographic factors that are associated with HIV mortality would be especially useful. Again, I was impressed by a creative approach to skirting a resource issue, in an effort to create more implementable solutions for public health problems.
Using social factors to identify HIV mortality risk serves a second purpose in my mind as well. By looking at the patient’s lives, the cases became more human. No longer are you thinking of clinical cases, with CD4 counts and time on ART, but now you’re looking at their lives. Religious affiliations, types of care sought, homelessness…these are risk factors that turn cases into people. Perhaps most striking was the relationship between mental health and risk of HIV mortality: individuals at risk of depression have a significantly higher mortality risk than those that do not have depression. Mental health awareness is incredibly important, and should not be overlooked in any public health campaign.
Overall, I can’t help but to reflect on the fact that when researchers use interdisciplinary, creative, and novel approaches, progress leaps forward on multiple fronts. Combining efforts can stretch resources farther, grant novel insight, and find novel solutions to long standing problems.