| Literature DB >> 23936578 |
Martial L Ndeffo Mbah1, Eric M Poolman, Katherine E Atkins, Evan W Orenstein, Lauren Ancel Meyers, Jeffrey P Townsend, Alison P Galvani.
Abstract
BACKGROUND: Epidemiological data from Zimbabwe suggests that genital infection with Schistosoma haematobium may increase the risk of HIV infection in young women. Therefore, the treatment of Schistosoma haematobium with praziquantel could be a potential strategy for reducing HIV infection. Here we assess the potential cost-effectiveness of praziquantel as a novel intervention strategy against HIV infection.Entities:
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Year: 2013 PMID: 23936578 PMCID: PMC3731236 DOI: 10.1371/journal.pntd.0002346
Source DB: PubMed Journal: PLoS Negl Trop Dis ISSN: 1935-2727
Figure 1Outline of FGS-HIV transmission model.
The flow between epidemiological classes for the transmission dynamics of FGS and HIV. Individuals enter the system at age 15, and exit at age 50. , , , and denote, respectively, the number of women uninfected by HIV and FGS, infected with FGS, infected with HIV, and infected with both HIV and FGS. and denote, respectively, the number of men uninfected and infected with HIV.
Estimates of the parameters used in our dynamic HIV-FGS model (Figure 1).
| Variable | Meaning (units) | Prior distribution [ref] | Posterior distribution: median (95% CI) |
|
| Annual growth rate rural population | 0.034 | N/A |
|
| Duration of sexual activity (year-1) | 1/35 | N/A |
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| Enhanced HIV transmission due to FGS | Uniform(0,20) | 5.9 (3.8–9.1) |
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| Probability of FGS given childhood infection | Uniform(0.33,0.75) | 0.47 (0.38–0.56) |
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| Probability of acquiring FGS during adulthood | Uniform(0.005,0.05) | 0.008 (0.006–0.009) |
|
| Duration of HIV infection (years) | Uniform(7.5,12.5) [33, 48] | 10.7 (8.1–12.1) |
|
| Number of sex acts in partnerships per year for high-risk group | Uniform(15,150) [48] | 128 (95–148) |
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| Number of sex acts in partnerships per year for low-risk group | Uniform(50,248) [48] | 69 (28–137) |
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| HIV transmission rate per sex act | Uniform(0.0006,0.004) | 0.0022 (0.0009–0.003) |
|
| Mixing between sexual risk groups | Uniform(0.2,0.9) [49] | 0.44 (0.22–0.62) |
|
| Extent to which males determine the pattern of sexual partnerships formation | Uniform(0.2,0.8) [48] | 0.67 (0.50–0.78) |
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| Initial partner change rate: women (year-1) | Triangular(0.66,2.4,0.9) [48] | 1.27 (0.7–2.2) |
|
| Initial partner change rate: men (year-1) | Triangular(1.1,3,1.2) [48] | 1.9 (1.2–2.8) |
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| Fraction of women in high-risk group | Uniform(0.05,0.6) [48] | 0.20 (0.15–0.24) |
|
| Fraction of men in high-risk group | Uniform(0.10,0.75) [48] | 0.34 (0.22–0.49) |
|
| Relative rate of partner change: high-risk versus low-risk group | Uniform(1,100) [48] | 10.7 (2.5–21.8) |
|
| Reduction rate of partner change | Uniform(1,50) | 6.5 (3.5–9.0) |
|
| Year HIV epidemic starts | Uniform(1978,1985) [48,50] | 1981 (1980–1982) |
These parameter estimates produced the best fit of our dynamic model to epidemiological data for HIV and FGS prevalence and co-infection among rural Zimbabwean women [3], [7]. The dynamic model was fit to these data using a Markov Chain Monte Carlo method, which allowed us to calculate distributions of possible values for each of these parameters. We present here the mean of these distributions and their associated 95% credible intervals. The Brooks-Gelman-Rubin (BGR) method was used to monitor convergence of iterative simulations. Convergence was achieved when the upper limit of the credible interval of the BGR diagnostic statistic for a given parameter <1.2 [51].
Model fit to data.
| Kjetland et al | Model | |
|
| 46.1% (41.8–50.5) | 45.5% (43.6–48.1) |
|
| 28.1% (24.0–32.5) | 29.6% (26.0–32.0) |
|
| 2.1 (1.2–3.5) | 2.1 (1.5–2.8) |
FGS prevalence, HIV prevalence, and odds ratio of the association between FGS and HIV from the 1999 cross-sectional epidemiological study among rural Zimbabwean women [3], [7] and model predictions.
Figure 2Comparison of model predictions to Zimbabwe antenatal clinic data for non-urban areas [17].
The solid line represents the yearly HIV prevalence as estimated by our model from baseline epidemiological parameters (dotted lines are the 2.5th and 97.5th percentile values). Empirical HIV prevalence is shown as stars (error bars are the 95% confidence intervals). The model was validated from antenatal clinic data not originally used for model parameterization.
Figure 3Cost-effectiveness of school-age intervention for the base case analysis.
The number of HIV cases averted (A,B), the cost per HIV cases averted (C,D), and the net savings (E,F) were computed for different efficacies of mass praziquantel administration in reducing FGS (B,D,F) and the mitigated risk of HIV infection per sexual act (A,C,E).
Figure 4Partial rank correlation coefficients (PRCCs).
A parameter was considered to be important in affecting the effectiveness of mass drug administration with praziquantel for impact on HIV transmission if |PRCC|>0.4. Specifically, probability of acquiring FGS from adult infection and the annual number of sex acts in low risk partnerships were the most important parameters for the first scenario (A), and the coefficient by which FGS enhances HIV transmission rate per sex act and the annual number of sex acts in low-risk partnerships were the most important parameters for the second scenario (B).