Some recent catch-up reading that’s caught my eye; all HIV stuff it turns out, and as a bonus, all freely accessible:
- How important are viral introductions vs. local transmission for sustaining an epidemic in rural Uganda? In Rakai, it seems, pretty important. I really liked how the authors triangulate spatial, phylogenetic and mathematical modelling approaches to build a solid narrative. And the topic seems really important in the context of targeting key subpopulations within generalized epidemics – something that is rising up the prevention agenda, apparently/hopefully. The primary author also has some recent work (presented at CROI this year) on which partner brings HIV into stable couples; an area in desperate need of good data.
- Unconditional cash to caregivers of children affects age of sexual debut, but not other risk behaviours, in Kenya. This study starts to get at which behaviours are, and are not, driven by economic factors – as opposed to cultural or other norms. I would have liked to have seen data on marriage age too, but since that doesn’t typically happen while the child is within the home, perhaps that’s too distant an outcome.
- A review of some heterogeneities in infection dynamics that provoked some thoughts, although not about modelling, which is the nominal reason for the piece. Notably, the authors highlight that there may well be heterogeneity in susceptibility, which will practically act to fragment sexual transmission networks as less-susceptibles act more like recovered individuals. And that alloimmunity to a partner’s expressed antigens may mean that infection risk from an ongoing partner recently infected may be less than expected, while that from a new partner with a chronic infection may be higher than expected (due to no acquired alloimmunity). I must admit this is my first introduction to alloimmunity – it looks like there’s a whole world of research out there on this, with some interesting paradoxical results. Happy reading.
This caught my eye because it brings together things I’ve seen in two very different spheres of research – stress in social determinants research and HIV in Cape Town (I also know some of the authors – it’s always social networks in the end…).
No in-depth analysis from me here, but it’s really interesting how widely teleomere research is going and how it is being used to quantify the impact of a wide range of exposures on the broadest of health outcome measures (i.e. ‘biological aging’). I don’t know enough details about teleomeres to know how we meaningfully convert length to life expectancy (or quality of life), but it both bothers me a little that we may be focusing on something because it is measurable, and intrigues me that we may be able show previously only-hypothesised stress effects of HIV, and in particular antiretroviral treatment, on the body.
Maybe the most interesting line for me, showing my personal interests I guess, was:
Socio-economic factors were not associated with biological aging in HIV-infected participants
My prior would have been that this is in contrast to the general population, however a recent review suggests the evidence for a link between SES and teleomere length is mixed. And in this study, SES was unrelated for both those with and without HIV.
Still lots of interesting work out there to do on this…
Clearly much water (23 weeks worth, apparently) has passed under the bridge since I last posted on new papers crossing my rss/email desk. So here are the latest batch:
- I am more than a little fascinated by the interplay of race, SES, gender and any other stratifier you can mention in determining infection risks. And in the field of STIs, I appear not to be the only one. One angle on this is to look at multiple low-power identities and see how they interact. For example, here‘s a paper that focuses on the intersection of race/ethnicity and sexual orientation. Risks definitely rise with multiple minority statuses, but the pattern is non-simple and varies by sex. It’s never as simple as you’d think.
- More on concurrent partnerships in Africa. A recent study found no association between (self-reported) concurrent partnerships and HIV incidence in rural South Africa and while it is generally accepted that concurrency can theoretically drive an HIV epidemic, empirical evidence remains scant that it does so. One reason for this may be that while concurrency is risky, its prevalence is low. This idea is supported by a paper out of Malawi from last year which finds that concurrency is long-lasting when it occurs, but that it occurs infrequently (only in 9% of the sample). Lots more evidence is needed on this, but these are the right questions to be asking.
- This one is only “new to me”. Dynamic models of sexual relationships (and other contact networks, I think that sexual networks are simpler than most) need to be a big new field in ID Epi. If that’s going to happen we (public health people) are going to need to read lots of network analysis stuff (aside: here‘s an intro from Nick Christakis and collaborator Kirsten P Smith (meta-aside: Smith’s disseration from Penn looks really interesting – international comparisons of STI rates, but I can’t see it published yet). If you want more theoretical details, James Moody – one of the key people behind the Add Health network work mentioned in the above article – wrote a paper in Social Forces a decade ago, which outlines things nicely. Beach reading, if I ever made it to the beach. And enjoyed reading once there.
Next time I’ll try and make these papers a little more up-to-date, but this’ll have to do for now.
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.
This week I have three possibilities to tantalize you with, one of which I am immediately set against, but feel I should read.
1. This piece in AIDS Care worries me for two surface reasons. First, the abstract suggests that it is a quantitative analysis of DHS data being published in AIDS Care, a journal I usually turn to for depth and richness, not number crunching. But I would never write something off based only on this. Of more concern is the line “Contrary to the public health literature, women of high SES were also vulnerable to HIV risk”. I am pretty there is a large literature highlighting this relationship already, which leads me to question the level of background research conducted.
2. A reminder that prevention interventions are acutely context-dependent.
3. I find it strange/disturbing how infrequently we consider the disease context of a community in measuring risk factors for HIV across large areas. This paper from Zimbabwe, via UNC Chapel Hill, is a nice reminder of the importance of cross-level interactions.
Disclaimer. These posts are based on my reading of titles and abstracts, and all papers may be of much greater/lesser interest/quality than I have concluded based on reading 200 words or fewer.
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.