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Since more than half the population were in fact living above 1,000 m, this explained a very large part of the overall excess prevalence.
Table 1 shows the excess prevalence by age and sex.
Therefore we use HIV excess prevalence [88] as an additional measure of the synergy.
Analogously, we define HSV-2 excess prevalence which provides a measure of the impact of HIV on HSV-2 epidemiology.
We found that the excess prevalence of HSV-2 due to HIV is much smaller than that of HIV due to HSV-2 (Figures 1C and 2C).
If HSV-2 prevalence in Kisumu was at 21% as in Cotonou, Benin, then HIV excess prevalence would have been 6.1% (if PECAcq = 4) or 8.4% (if PECAcq = 9).
Similar(28)
Figure 1B and 1C display the PAFSus, PAFInf, PAFSus+Inf, and HIV and HSV-2 excess prevalences.
The HIV and HSV-2 excess prevalences in Figure 1C illustrate how HSV-2 has played a major role in fuelling HIV spread directly and indirectly particularly in the early stage of the epidemic though HIV has limited impact on HSV-2 prevalence throughout the HIV epidemic.
The excess HIV prevalence is highest among women 25 years and older and among men 30 years and older.
The estimated "excess" HIV prevalence due to ART in 2008 was highest among women 25 years and older and among men 30 years and older.
The above results indicate that at the time of the Four City study (1997 1998), the interaction has contributed 14% in excess HIV prevalence if PECAcq = 4 (Figure 1C) and 22% if PECAcq = 9 (Figure 2C).
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