Awareness and Flu behaviors: Geography and Information

Papers like this make me happy. If we’re ever going to get to the bottom of causal mechanisms, this is the empirical spade work we need to start doing (IMHO).  And really it’s pretty simple to do.

Here the authors, Caroline Rudisill and colleagues, have looked at survey data taken around the time of the H1N1 outbreak to see if individuals changed their behaviour (putatively) in response to the epidemic.  The clever part, for me, is that the focus is not on social distancing or other measures often considered useful by Public Health professionals, but rather on misplaced/unhelpful behaviour – here the consumption of poultry products.

First, the authors appear to find that, as the old saying goes, a little knowledge is a dangerous thing.  That is, if respondents got one of six questions about human risks associated with avian influenza right, their likelihood of decreasing poultry products rose (note however that only 2% of respondents got all 6 true/false questions wrong).  As the number of correct answers rose, the effect reversed and those answering 4,5 or 6 questions correctly had all increased their poultry consumption compared to 6 months prior.

Second, the authors focused on whether the occurence of H1N1 cases in your country was associated with behaviour change.  In a model that also contained Knowledge variables, the authors found a strong effect for cases having occured in your country, but in the direction of consuming less poultry (Knowledge remained protective).

All of which suggested to the authors that correct behaviour change is possible amongst sensitized populations (i.e. those aware of the health risk), if the right information is provided:

Once individuals feel more personally at risk or as a risk ceases to be abstract, they become more likely to take measures they believe to reduce their risk exposure. Therefore, there is still a role for knowledge to successfully prevent alarmist behaviors even when there are positive identifications of H5N1 within one’s country of residence or in a bordering country, as long as the virus has not occurred in humans yet.

Obviously there are many limitations to this work – not least the cross-sectional rather than longitudinal nature of the study.  But this is a really nice example of applying thought to readily available data to make an empirical contribution.

Aside: Of course, from a social epi viewpoint, I’d be really interested in knowing how all these factors are patterened by SES, in particular education and income (if poultry prices fell, would poorer people consume more?  would this vary by awareness levels?), but maybe that’s something for me to look into…

Citation: Rudisill C, Costa-Font J, Mossialos E.  Behavioral adjustment to avian flu in Europe during spring 2006: The roles of knowledge and proximity to risk.  Social Science & Medicine, forthcoming.

Inequality more relevant than Poverty for HIV in Africa

Now here is an article was sufficiently interesting to raise me from my publishing lethargy and get me to WordPress.  The research focuses on socioeconomic correlates of HIV in Africa.  It follows from past research suggesting that wealth/poverty is not driving the epidemic, as rates are (at least initially) higher amongst well-off persons in this continent.

For her dissertation, on which this paper is based, Dr Fox (no, not that one) dug into the DHS datasets from across sub-Saharan Africa and built a mammoth of a multilevel model that allowed for individual, regional and national level income wealth and wealth inequality.

There are many interesting findings in the paper, but I wanted to highlight two.  First, the headline result is that regional inequality predicts HIV infection net of personal wealth, although it is not strongly mediated by circumcision or sexual behaviour.  This is of great interest to me as someone with a working interest in the impact and mechanisms of inequality on sexually transmitted diseases, although it does beg the question of what the causal mechanisms (or confounders) might be.

Perhaps more interesting, however, is found in the nuance seen when stratifying by regional wealth:

The findings also reveal a paradox that supports a dynamic interpretation of epidemic trends: in wealthier regions/countries, individuals with less wealth were more likely to be infected with HIV, whereas in poorer regions/countries, individuals with more wealth were more likely to be infected with HIV.

I read this as supporting the idea of connectivity being key to getting one into the sexual networks that put one at risk, and the idea that having resources (e.g. wealth, knowledge) once in this network is key to reducing the risk (either by leaving the network or protecting yourself within it).  Note that ‘wealthier regions’ tends to mean urban, often capital.

There is a lot more work to be done on the dynamics of the relationship between socioeconomic factors and HIV infection, but this is an important step along the road.

Citation: Fox, AM. The HIV-poverty thesis re-examined: Poverty, wealth or inequality as a social determinant of HIV infection in sub-Saharan Africa? Journal of Biosocial Sciences, epub ahead of print. doi:10.1017/S0021932011000745 . Link.

Better methods for measuring HIV-related deaths

I remember hearing David Bourne talk while I was taking classes at UCT a few years ago. He was a forceful speaker, but the most powerful memory I have of the talk was his presentation of the data that later became a 2005 paper outlining how deaths from HIV were being undercounted, and adjusting for this using the clever approach of looking at trends in likely opportunistic infections (OIs) such as pneumonia and tuberculosis.

The idea was that increases in these causes of death – not generally things we would expect an increase in given South Africa’s level and growth direction of economic well-being – over and above some pre-serious-HIV-mortality timepoint (they use 1996, which makes sense given the selective vital statistics data available prior to fully-representative democracy in 1994) could probably be associated with HIV infection. They match the estimates found in this way with existing dynamic mathematical models of the epidemic, and suggest that death certificates had been underestimating HIV-related mortality by more than 60%. This was particularly important at the time due to the combative mood in the country over the magnitude of the HIV epidemic, with some suggesting that the problem was not as significant since few people were being recorded as dying of HIV or AIDS.

Now, there is a possibly even cleverer study in a recent edition of the Bulletin of the WHO. They note that the estimates made in 2005 relied on a lack of misclassification of causes of death in 1996 – the baseline year. To account for potential misclassifcation, they use worldwide data to estimate the relative risk of death for each age group for various categories of cause of death, compared to a reference age range of 65 to 80, where HIV infection is expected to have been irrelevant at any meaningful level. The authors then compare these global relative death risks (RDR) to South African RDRs in each cause of death category. Those which stand out as different are marked down as potential sources of misclassified deaths. The differences between the worldwide and South African RDRs were then used to estimate the number of misclassified deaths.

And the result: “this study suggests that during 1996–2006 as many as 94% of all HIV/AIDS deaths in the country were being misclassified, especially among young to middle-aged females and among males in the middle-aged and older groups.” This is even more than the 2005 study suggested. But doesn’t really seem to qualitatively change our impressions. Nevertheless, and despite various caveats the authors’ note, this was a nice attempt to further strengthen a methodology to evaluate the level of misclassification present in cause of death notification. And it reminded me of a good, and always thoughtful, man who is sadly no longer with us.

Concurrency vs. Rapid Partner Change

So here’s an interesting paper. The authors looked at self-reported Sexually Transmitted Infections (STIs) and self-reported sexual history over the past year in a cross-sectional study in St. Petersburg, Russia. This design might raise a red flag or three regarding the reliability of the data, but unless you can tell me how misreporting is likely to be correlated with (what I think is) the key finding, that doesn’t undermine their work all that much.

What they report is that short relationship gaps (1-90 days) put people at higher risk than overlapping (concurrent) relationships. The authors suggest:

The present study’s findings may seem counterintuitive, but we might explain them with a scenario in which people with short partnership gaps have more frequent sexual intercourse with their STI-infected partner than do individuals with overlapping relationships, provided that both groups have similar rates of condom use, which was the case in our study

Specifically, they argue that concurrent partnerships require you to be having sex with multiple people at once, and thus you can’t give each partner as much attention as you can in a serially monogamous relationship.

I thought that this paper was interesting because it helped me to remember that while concurrency is dangerous for STIs, closely packed serial relationships can be just as bad, if the gap between relationships is less than the incubation window period plus the period of high infectiousness around breakout. This is especially true if, as seems reasonable (although the interwebs/PubMed is not helping me with longitudinal evidence of sexual behaviour within relationships – sounds like a paper to me) people have more frequent sex at the beginning of relationships.

An Aside: Interestingly, another paper from the same study noted that your partners’ concurrency status is associated with your disease status, but given that, yours doesn’t actually matter. As ever, who you’re involved with is more important than how many you’re involved with…

Paper: Zhan W, Krasnoselskikh TV, Golovanov et al. Gap between Consecutive Sexual Partnerships and Sexually Transmitted Infections Among STI Clinic Patients in St Petersburg, Russia. AIDS and Behavior, 2011; epub ahead of print. DOI: 10.1007/s10461-011-9932-z. Link (gated).