Sociobehavioral characteristics & HIV epidemics

A longitudinal study in 29 Sub-Saharan African countries



Link to
Sub-Saharan African continent
  • Exceedingly diverse in terms of sociobehavioral characteristics
  • Reflected in wide variations in levels of HIV epidemics
Previous research
  • Limited geographical range:
    • Single countries
    • single regions
    • Specific key populations
  • Limited range of risk-factors:
    • Narrow set of risk factors
    • Specific HIV-related attributes
"Clusters of sub-Saharan African countries based on sociobehavioural characteristics and associated HIV incidence"

Aziza Merzouki, Janne Estill, Erol Orel, Kali Tal, Olivia Keiser

doi: https://doi.org/10.1101/620450

Main findings:
Main limitations
  • Impossible to determine precedence and causality between SB characteristics and HIV incidence
  • Epidemiology of HIV progress over many years/decades
In this study:
  • Evolution of sociobehavioral characteristics of 29 countries over time
  • Evolution of HIV transmission dynamics over the same time period
  • Explore link between the two
Study only as good as data used
Ideally:
  • Historical data would be easy to find
  • Countries would have many surveys spanning many years
  • Our choice of 46 sociobehavioral indicators would always be present
Reality:
  • Historical data would be easy to find
  • DHS data prior to 2000 was unreliable
  • Countries would have many surveys spanning many years
  • Countries have few and irregularly timed surveys
  • Our choice of 46 sociobehavioral indicators would always be present
  • Many latent/missing values in the surveys
  • Many HIV indicators only started in the early 2000s
Strategy
  • Use only surveys from 2000 and later
  • Missing data
    • Find alternate source for indicator
    • Impute missing data
Resulting data
  • 83 surveys from 29 countries (with each between 1 and 5 surveys between 2000-2018)
  • 46 indicators

Analysis of sociobehavioral characteristics

  • Reduce dimensionality using Principal Component Analysis
  • Visualize surveys on the reduced 2 dimensional PCA space
  • Cluster countries using concensus clustering technique

Analysis of transmission dynamics

Individual risk of acquiring HIV given by: $\sum_{i=1}^{j} r_{i} * \rho_{i}$
And so
$$\text{New infections[n]} = \beta[n]*\frac{I[n]}{N[n]}*S[n]$$ where:
  • $r$ is ith exposure event
  • $\rho$ is the chance of that exposure event leading to infection
  • $S[n] = \text{number of susceptible in given year}$
  • $I[n] = \text{number of infected in given year}$
  • $N[n] = \text{total population in given year}$
  • $\beta[n] = r * \rho = \text{effective contact rate}$

Effective contact rate $\beta$

$$\text{New infections[n]} = \beta[n]*\frac{I[n]}{N[n]}*S[n]$$ Data from UNAIDS gives $HIV_{Incidence}$ in "per 1000" so: $$HIV_{Incidence}[n] = \text{New infections[n]} * \frac{1000}{N[n]}$$ $$\iff HIV_{Incidence}[n] = \beta[n]\frac{I[n]}{N[n]} \frac{S[n]}{N[n]} * 1000$$ $$\beta = \frac{HIV_{Incidence}}{HIV_{Prevalence}*(1-HIV_{Prevalence})}$$
Results
Different levels of HIV epidemics seen today unlikely to be a direct result of different evolutions of sociobehavioral characteristics
  • Difference in levels of HIV incidence across 3 clusters present since early 1990s
  • Effective contact rate evolved in a similar manner across clusters over same period
Different levels of HIV epidemics today likely to be the result of different initial conditions in nascent epidemics
  • Small differences in inital effective contact rate can lead to large differences over course of epidemics
  • Evidence for this in effective contact rate of 1990
  • Can be hypothesized male circumcision played a role in reducing effective contact rate in countries with high rates of male circumcision
ART coverage and HIV testing likely to bridge the gap across in HIV epidemics
  • Larger increase in ART coverage and HIV testing since 2005 in countries of Eastern and Southern Africa differences than countries of other 2 clusters
  • Resulting in faster decrease of effective contact rates of those countries
  • Over time, will likely lead to faster control of the epidemics in those countries
Further research
  • Reconstructing effective contact rate prior to 1990
  • Reconstructing sociobehavrioal profiles of countries prior to 2000
Limitations
  • Effective contact rate has the advantage of allowing comparisons across HIV epidemics but relis on both HIV incdence and HIV prevalence which may have large errors
  • Research has shown using a subset of sociobehavrioal indicators can produce better results
  • Nationally-aggregated data prone to ecological fallacy
  • Nationally-aggregated data prone to overlooking salience of high-risk key-populations