Links roundup

A few things that passed across my rss feed/tweetdeck/other input strand recently:

  • Thoughts on predicting which mega-urban areas are most vulnerable to epidemics (h/t Matt Watson, @BioAndBaseball).
  • Kings Fund study finds increasing inequality in behavioural risk factors by income & education (cf Victora’s Inverse equity hypothesis).
  • A new data visualization tumblr from the World Bank.
  • An brief-ish article (paywalled, sorry) that promos a new book on envisioning Public Health ecologically, from two UK Food Policy researchers.
Advertisements

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.

Papers of the Week: 01/2012

I’ll keep this brief, since I should be doing far less useful things than posting while on holiday.  But here are three that caught my eye:

1. Qualitative research on sexual relationships is so important in interpretting sexual behaviours.  Especially partner types, as discussed here by Noar and colleagues.  I do note that many examples of this type of study focus on minorities (often by sexual preference or race/ethnicity).  One day I should probably look at the variation in findings by such factors, but I’m lazy when it comes to meta-analyses or just pulling together whole literatures.

2. I often find interesting, but not immediately useful, stuff in the Milbank Quarterly.  The latest edition has a piece using the Earned Income Tax Credit as an IV to measure the impact of income change on health status (both self-reported) at the individual level.  Larrimore finds a correlation in levels between income and health status, but not an effect in changes (i.e. more income to better health).  As the author notes, etiologic period is key here, and these effects are short-run, but it’s always good to be reminded that correlation and causation are very different things.

3.  And finally a quickie – state inequality is associated with a higher familial burden for children with special healthcare needs in the USA.  Not shocking, but another brick in the evidential wall that unequal states are less supportive than others.

NB.  As ever, I haven’t read these papers in detail and cannot vouch for them – they are just the abstracts/titles that caught my eye this week.