We examined levels and variations in HIV discordancy measures among stable sexual partnerships across 20 countries in SSA by statistically analysing DHS data. Our analyses revealed that at least 50% of the variability in the two key discordancy measures (
) can be explained by differences in HIV prevalence. Both discordancy measures showed a continuous gradation with a steady decline associated with increasing HIV prevalence. These results can be viewed for simplicity as showing two distinct patterns of HIV discordancy for countries with low compared with high HIV prevalence.
In low-prevalence countries, the majority of stable partnerships with at least one HIV-infected individual
were found discordant and a minority were concordant positive. Meanwhile, about half of these partnerships were discordant in high-prevalence countries. Similarly, about one of three HIV-infected individuals was engaged in an SDP in low-prevalence countries (ie,
) compared with about one of five in high-prevalence countries. Our results are in line with distinct empirical evidence from different communities in SSA suggesting a range for
between 30% and 90%, with a pattern of about half of partnerships affected by HIV being discordant in high-prevalence areas.3
Our analyses also revealed that out of every 100 stable partnerships, four are affected by HIV in low-prevalence countries and 29 in high-prevalence countries. Out of all stable partnerships, about one in every 37 is discordant
in low-prevalence countries compared with about one in every seven in high-prevalence countries. These results agree with existing empirical evidence suggesting that >10% of partnerships in high-prevalence areas may be discordant.2
Moreover, our findings show that approximately two of every 100 sexually active adults
are engaged in an SDP in low-prevalence countries compared with about seven of every 100 in high-prevalence countries.
In agreement with findings of Eyawo et al14
for 14 countries in SSA, our analyses revealed that women are equally likely as men to be the infected partner in a discordant partnership (46% (95% CI 41% to 51%) in Eyawo et al
vs 49.4% (95% CI 44.5% to 54.3%) in our study). Assuming that HIV male to female transmission probability is equal to that of female to male transmission probability, as suggested by empirical data,18
these results suggest that women may be equally likely as men to bring the infection to the stable partnership from sources external to the partnership.
While our study has described discordancy patterns, the nature and balance of drivers behind these observed patterns are not clear and may only be subject to speculative interpretation. Different hypotheses may explain the observed trends. The potential variability in HIV transmission probability per coital act across SSA affects the likelihood of an SDP becoming concordant positive and may explain part of the observed patterns. HIV transmission is dependent on multiple biological factors such as male circumcision,19–21
presence of other sexually transmitted diseases,22
presence of tropical co-infections that increase HIV viral load,24
and host immunology.25
Behavioural factors and uptake of prevention interventions such as frequency of coital acts, temporal changes in sexual behaviour and condom use can also impact the likelihood of transmission within a partnership. All these factors may vary across different settings in SSA.
The likelihood of infection by external partners might also contribute to the observed variations in discordancy measures across SSA. Indeed, the likelihood of acquiring HIV from a source external to the SDP increases with HIV population prevalence.
The chance of partnership formation between infected and uninfected partners may also explain part of the observed dynamics. Assuming random mixing in a population with an HIV prevalence of ‘p
. Accordingly, the dependence on HIV prevalence of these expressions may explain part of the scale and variability of the discordancy measures.
One hypothesis might be that HIV incidence rate, that is, the annual risk of infection for an individual from all sources, might have declined in some countries with high HIV prevalence. In a setting where HIV incidence rate is declining, it is possible that the rate at which partnerships involving uninfected partners become discordant is less than the rate at which existing SDPs become concordant positive. This potential imbalance in the flow between concordant negative partnerships becoming discordant versus SDPs becoming concordant positive can lead to lower
as existing SDPs become concordant positive at a faster rate than new partnerships become discordant. This suggestion is consistent with empirical evidence for declining HIV incidence in several countries at high HIV prevalence.1
Mathematical models would be able to examine whether this could be consistent with a fuller analysis of the data and through an examination of data collected in future surveys.
Several study limitations might have affected our findings. First, our selection of the DHS survey for the different countries was constrained by the availability of HIV biomarker information at any particular survey. This limited our ability to consider more countries in SSA for analysis with more recent DHS surveys. In addition, we explored the variation in discordancy measures by HIV prevalence across different countries in SSA through an ecological analysis which uses aggregate rather than individual level data and, hence, limits our ability to establish causality. Furthermore, intra-country epidemic heterogeneity is present and may affect the validity of a national-level analysis using DHS data.
Given the multiple logistical difficulties in conducting DHS surveys, some of our discordancy measures may be biased due to inherent biases in the data such as the variability in response rates to HIV testing28
where, in countries such as Malawi and Zimbabwe, low response rates of 67% and 70%, respectively, have been recorded. Moreover, the higher likelihood of women to undertake HIV testing might have also reduced the probability of identifying partnerships affected by HIV.
Our findings might also be sensitive to non-random selection bias where urban populations and individuals with prior HIV testing may be more likely to refuse testing.30
We conducted further comparative analyses to detect underlying non-random epidemiological or socio-demographic differences between couples with and without complete HIV serostatus information. The comparisons indicated that for most countries, no significant differences exist between both groups with respect to age. Yet, couples with higher education and living in urban areas were less likely to have complete HIV serostatus information. Despite these limitations, the DHS surveys are among the most methodologically rigorous surveys available in SSA.32
Discordant couples are a key population for HIV prevention programmes and several major randomised clinical trials have established substantial efficacies of several prevention interventions that could benefit this population group.8–11
Our findings have implications for how such prevention interventions can be translated into programmes for SDPs, and in the population at large, and form a base upon which the impact of future HIV intervention programmes can be quantified. Our results suggest complex considerations for the implementation of HIV prevention interventions among SDPs. While the majority of partnerships affected by HIV are discordant in low HIV prevalence settings, their enrolment in prevention interventions constitute a major logistical challenge since the absolute number and the proportion of all sexual partnerships that are discordant are small. Conversely, while the number and proportion of all sexual partnerships that are discordant are large in high-prevalence settings, only about half of partnerships affected by HIV are discordant and only about a fifth of all HIV-infected individuals are in SDPs.
Although prevention interventions aiming at the protection of the seronegative individual in an SDP would reduce HIV incidence among discordant partnerships, quantifying the impact of such interventions at the population level requires further empirical epidemiological evidence and thorough mathematical modelling analyses. Moreover, the feasibility of prevention interventions targeting individuals engaged in SDPs must factor the abundance of discordancy, the ease and expense of reaching these individuals and the labour-intensiveness and long-term effectiveness of these interventions.
- At least 50% of the variation in HIV discordancy can be explained by differences in HIV prevalence.
- In high HIV prevalence countries, which make up the large majority of HIV-infected individuals, most HIV-infected individuals are not in stable discordant couples.
- In high HIV prevalence countries, a large fraction of stable partnerships are affected by HIV and half are discordant.
- In low HIV prevalence countries, a small fraction of stable partnerships are affected by HIV, but the vast majority of them are discordant.
- Complex considerations exist for rolling out prevention interventions targeting discordant partnerships.